[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/learning-python-with-a-little-help-from-ai-code-suggestions":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Michael Friedrich":701,"blog-related-posts-en-us-learning-python-with-a-little-help-from-ai-code-suggestions":715,"blog-promotions-en-us":757,"next-steps-en-us":809},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/learning-python-with-a-little-help-from-ai-code-suggestions.yml","Learning Python With A Little Help From Ai Code Suggestions",[7],"michael-friedrich",null,"ai-ml",{"slug":11,"featured":12,"template":13},"learning-python-with-a-little-help-from-ai-code-suggestions",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Learning Python with a little help from AI","Use this guided tutorial, along with GitLab Duo Code Suggestions, to learn a new programming language.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663918/Blog/Hero%20Images/aipower.jpg","2023-11-09","Learning a new programming language can help broaden your software development expertise, open career opportunities, or create fun challenges. However, it can be difficult to decide on one specific approach to learning a new language. Artificial intelligence (AI) can help. In this tutorial, you'll learn how to leverage AI-powered GitLab Duo Code Suggestions for a guided experience in learning the Python programming language with a practical hands-on example.\n\n- [Preparations](#preparations)\n  - [VS Code](#vs-code)\n  - [Code Suggestions](#code-suggestions)\n- [Learning a new programming language: Python](#learning-a-new-programming-language-python)\n    - [Development environment for Python](#development-environment-for-python)\n    - [Hello, World](#hello-world)\n- [Start learning Python with a practical example](#start-learning-python-with-a-practical-example)\n    - [Define variables and print them](#define-variables-and-print-them)\n    - [Explore variable types](#explore-variable-types)\n- [File I/O: Read and print a log file](#file-io-read-and-print-a-log-file)\n- [Flow control](#flow-control)\n    - [Loops and lists to collect files](#loops-and-lists-to-collect-files)\n    - [Conditionally collect files](#conditionally-collect-files)\n- [Functions](#functions)\n    - [Start with a simple log format](#start-with-a-simple-log-format)\n    - [String and data structure operations](#string-and-data-structure-operations)\n    - [Parse log files using regular expressions](#parse-log-files-using-regular-expressions)\n    - [Advanced log format: auth.log](#advanced-log-format-authlog)\n    - [Parsing more types: Structured logging](#parsing-more-types-structured-logging)\n- [Printing results and formatting](#printing-results-and-formatting)\n- [Dependency management and continuous verification](#dependency-management-and-continuous-verification)\n    - [Pip and pyenv: Bringing structure into Python](#pip-and-pyenv-bringing-structure-into-python)\n    - [Automation: Configure CI/CD pipeline for Python](#automation-configure-cicd-pipeline-for-python)\n- [What is next](#what-is-next)\n    - [Async learning exercises](#async-learning-exercises)\n    - [Share your feedback](#share-your-feedback)\n\n## Preparations\n\nChoose your [preferred and supported IDE](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-in-other-ides-and-editors), and follow the documentation to enable Code Suggestions for [GitLab.com SaaS](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-on-gitlab-saas) or [GitLab self-managed instances](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-on-self-managed-gitlab).\n\nProgramming languages can require installing the language interpreter command-line tools or compilers that generate binaries from source code to build and run the application.\n\n**Tip:** You can also use [GitLab Remote Development workspaces](/blog/quick-start-guide-for-gitlab-workspaces/) to create your own cloud development environments, instead of local development environments. This blog post focuses on using VS Code and the GitLab Web IDE.\n\n### VS Code\n\n[Install VS Code](https://code.visualstudio.com/download) on your client, and open it. Navigate to the `Extensions` menu and search for `gitlab workflow`. Install the [GitLab Workflow extension for VS Code](https://marketplace.visualstudio.com/items?itemName=GitLab.gitlab-workflow). VS Code will also detect the programming languages, and offer to install additional plugins for syntax highlighting and development experience. For example, install the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python).\n\n### Code Suggestions\n\nFamiliarize yourself with suggestions before actually verifying the suggestions. GitLab Duo Code Suggestions are provided as you type, so you do not need use specific keyboard shortcuts. To accept a code suggestion, press the `tab` key. Also note that writing new code works more reliably than refactoring existing code. AI is non-deterministic, which means that the same suggestion may not be repeated after deleting the code suggestion. While Code Suggestions is in Beta, we are working on improving the accuracy of generated content overall. Please review the [known limitations](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#known-limitations), as this could affect your learning experience.\n\n**Tip:** The latest release of Code Suggestions supports multiline instructions. You can refine the specifications to your needs to get better suggestions. We will practice this method throughout the blog post.\n\n## Learning a new programming language: Python\n\nNow, let's dig into learning Python, which is one of the [supported languages in Code Suggestions](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#supported-languages).\n\nBefore diving into the source code, make sure to set up your development environment.\n\n### Development environment for Python\n\n1) Create a new project `learn-python-ai` in GitLab, and clone the project into your development environment. All code snippets are available in this [\"Learn Python with AI\" project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-python-ai).\n\n```shell\ngit clone https://gitlab.com/NAMESPACE/learn-python-ai.git\n\ncd learn-python-ai\n\ngit status\n```\n\n2) Install Python and the build toolchain. Example on macOS using Homebrew:\n\n```shell\nbrew install python\n```\n\n3) Consider adding a `.gitignore` file for Python, for example this [.gitignore template for Python](https://gitlab.com/gitlab-org/gitlab/-/blob/master/vendor/gitignore/Python.gitignore?ref_type=heads).\n\nYou are all set to learn Python!\n\n### Hello, World\n\nStart your learning journey in the [official documentation](https://www.python.org/about/gettingstarted/), and review the linked resources, for example, the [Python tutorial](https://docs.python.org/3/tutorial/index.html). The [library](https://docs.python.org/3/library/index.html) and [language reference](https://docs.python.org/3/reference/index.html) documentation can be helpful, too.\n\n**Tip:** When I touched base with Python in 2005, I did not have many use cases except as a framework to test Windows 2000 drivers. Later, in 2016, I refreshed my knowledge with the book \"Head First Python, 2nd Edition,\" providing great practical examples for the best learning experience – two weeks later, I could explain the differences between Python 2 and 3. You do not need to worry about Python 2 – it has been deprecated some years ago, and we will focus only on Python 3 in this blog post. In August 2023, \"[Head First Python, 3rd Edition](https://www.oreilly.com/library/view/head-first-python/9781492051282/)\" was published. The book provides a great learning resource, along with the exercises shared in this blog post.\n\nCreate a new file `hello.py` in the root directory of the project and start with a comment saying `# Hello world`. Review and accept the suggestion by pressing the `tab` key and save the file (keyboard shortcut: cmd s).\n\n```markdown\n# Hello world\n```\n\nCommit the change to the Git repository. In VS Code, use the keyboard shortcut `ctrl shift G`, add a commit message, and hit `cmd enter` to submit.\n\nUse the command palette (`cmd shift p`) and search for `create terminal` to open a new terminal. Run the code with the Python interpreter. On macOS, the binary from Homebrew is called `python3`, other operating systems and distributions might use `python` without the version.\n\n```shell\npython3 hello.py\n```\n\n![Hello World, hello GitLab Duo Code Suggestions](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_python_code_sugestions_hello_world.png)\n\n**Tip:** Adding code comments in Python starting with the `#` character before you start writing a function or algorithm will help Code Suggestions with more context to provide better suggestions. In the example above, we did that with `# Hello world`, and will continue doing so in the next exercises.\n\nAdd `hello.py` to Git, commit all changes and push them to your GitLab project.\n\n```shell\ngit add hello.py\n\ngit commit -avm \"Initialize Python\"\n\ngit push\n```\n\nThe source code for all exercises in this blog post is available in this [\"Learn Python with AI\" project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-python-ai).\n\n## Start learning Python with a practical example\n\nThe learning goal in the following sections involves diving into the language datatypes, variables, flow control, and functions. We will also look into file operations, string parsing, and data structure operations for printing the results. The exercises will help build a command-line application that reads different log formats, works with the data, and provides a summary. This will be the foundation for future projects that fetch logs from REST APIs, and inspire more ideas such as rendering images, creating a web server, or adding Observability metrics.\n\n![Parsing log files into structured objects, example result after following the exercises](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_terminal_parsing_logs_and_pretty_print_results.png)\n\nAs an experienced admin, you can put the script into production and use real-world log format exmples. Parsing and analyzing logs in stressful production incidents can be time-consuming. A local CLI tool is sometimes faster than a log management tool.\n\nLet's get started: Create a new file called `log_reader.py` in the directory root, add it to Git, and create a Git commit.\n\n### Define variables and print them\n\nAs a first step, we need to define the log files location, and the expected file suffix. Therefore, let's create two variables and print them. Actually, ask Code Suggestions to do that for you by writing only the code comments and accepting the suggestions. Sometimes, you need to experiment with suggestions and delete already accepted code blocks. Do not worry – the quality of the suggestions will improve over time as the model generates better suggestions with more context.\n\n![Define log path and file suffix variables](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_log_reader_variables_01.png){: .shadow}\n\n![Print the variables to verify](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_log_reader_variables_02.png){: .shadow}\n\n```python\n# Specify the path and file suffix in variables\npath = '/var/log/'\nfile_suffix = '.log'\n\n# Print the variables\n\nprint(path)\nprint(file_suffix)\n```\n\nNavigate into the VS Code terminal and run the Python script:\n\n```shell\npython3 log_reader.py\n```\n\n![VS Code terminal, printing the variables](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_terminal_print_variables.png)\n\nPython supports many different types in the [standard library](https://docs.python.org/3/library/index.html). Most common types are: Numeric (int, float, complex), Boolean (True, False), and String (str). Data structures include support for lists, tuples, and dictionaries.\n\n### Explore variable types\n\nTo practice different variable types, let's define a limit of log files to read as a variable with the `integer` type.\n\n![Log file variable](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_log_reader_variables_03.png){: .shadow}\n\n```python\n# Define log file limit variable\nlog_file_limit = 1024\n```\n\nCreate a Boolean variable that forces to read all files in the directory, no matter the log file suffix.\n\n```python\n# Define boolean variable whether to read all files recursively\nread_all_files_recursively = True\n```\n\n## File I/O: Read and print a log file\n\nCreate a directory called `log-data` in your project tree. You can copy all file examples from the [log-data directory in the example project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-python-ai/-/tree/main/log-data?ref_type=heads).\n\nCreate a new file `sample.log` with the following content, or any other two lines that provide a different message at the end.\n\n```text\nOct 17 00:00:04 ebpf-chaos systemd[1]: dpkg-db-backup.service: Deactivated successfully.\nOct 17 00:00:04 ebpf-chaos systemd[1]: Finished Daily dpkg database backup service.\n```\n\nInstruct Code Suggestions to read the file `log-data/sample.log` and print the content.\n\n![Code Suggestions: Read log file and print it](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_read_log_file_and_print.png){: .shadow}\n\n```python\n# Read the file in log-data/sample.log and print its content\nwith open('log-data/sample.log', 'r') as f:\n    print(f.read())\n\n```\n\n**Tip:** You will notice the indent here. The `with open() as f:` statement opens a new scope where `f` is available as stream. This flow requires indenting )`tab`) the code block, and perform actions in this scope, calling `f.read()` to read the file contents, and passing the immediate value as parameter into the `print()` function.\n\nNavigate into the terminal, and run the script again with `python3 log_reader.py`. You will see the file content shown in the VS Code editor, also printed into the terminal.\n\n![VS Code terminal: Read log file, and print it](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_terminal_print_logfile_show_file_sample.png)\n\n## Flow control\n\nReading one log file is not enough – we want to analyze all files in a given directory recursively. For the next exercise, we instruct Code Suggestions to create an index of all files.\n\nPrepare the `log-data` directory with more example files from the [log-data directory in the example project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-python-ai/-/tree/main/log-data?ref_type=heads). The directory tree should look as follows:\n\n```shell\ntree log-data                                                             ─╯\nlog-data\n├── sample.log\n└── var\n    └── log\n        ├── auth.log\n        ├── syslog.log\n        └── syslog_structured.log\n\n3 directories, 4 files\n```\n\n### Loops and lists to collect files\n\nModify the `path` variable to use the value `log-data/`.\n\n```python\n# Specify the path and file suffix in variables\npath = 'log-data/'\nfile_suffix = '.log'\n```\n\nTell Code Suggestions to read all file paths in the directory into a list. After the collection loop, print the list of file paths.\n\n```python\n# Read all file paths in the directory into a list\n\n# Print the list of log file paths\n```\n\n![Code Suggestion, collect file paths](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_collect_files.png){: .shadow}\n\nAn example of a suggestion could look like this:\n\n```python\n# Read all file paths in the directory into a list\nimport os\n\n# Read all file paths in the directory into a list\nlog_files = []\nfor root, directories, files in os.walk(path):\n    for file in files:\n        if file.endswith(file_suffix):\n            log_files.append(os.path.join(root, file))\n\n# Print the list of log file paths\n\nprint(log_files)\n```\n\nLet's analyze what happens here: The `import os` statement is required to bring the `os` library into the current scope, and we are able to call `os.walk()` later. The `log_files` [list](https://docs.python.org/3/library/stdtypes.html#sequence-types-list-tuple-range) is initialized as an empty list using empty brackets. `os.walk()` expects a path as directory tree to start searching for files. The function returns a generator object, which will walk the directory tree recursively. The returned tuples include the collected files, which we want to access in a loop iteraor, using the `for` loop notation. When the file ends with the defined suffix, its full path is appended to the list. This is an optimized and complex example. Do not worry about understanding everything just yet - we will revisit this later.\n\n### Conditionally collect files\n\nModify the instructions and add the Boolean flag evaluation for `read_all_files_recursively` as an input to the glob() function.\n\n```python\n# Read all file paths in the top level directory into a list\n# If read_all_files_recursively is True, read all files recursively\n```\n\n![Code Suggestions, read files recursively conditionally](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_read_files_recursively_conditionally.png){: .shadow}\n\n```text\n# Read all file paths in the top level directory\nimport os\n\n# Read all file paths in the top level directory into a list\n# If read_all_files_recursively is True, read all files recursively\n\nlog_files = []\nfor file in os.listdir(path):\n    if file.endswith(file_suffix):\n        log_files.append(os.path.join(path, file))\n\nif read_all_files_recursively:\n    for root, directories, files in os.walk(path):\n        for file in files:\n            if file.endswith(file_suffix):\n                log_files.append(os.path.join(root, file))\n\n# Print log_files\nprint(log_files)\n```\n\nThe result is not optimal yet because it always executes the first loop, and optionally the second loop. This flow leads to duplicated results when the script is executed.\n\n```shell\npython3 log_reader.py\n\n['log-data/sample.log', 'log-data/sample.log', 'log-data/var/log/auth.log']\n```\n\nExperiment with Code Suggestions instructions to get a solution for the problem. There are different approaches you can take:\n\n1) A potential solution is to wrap the source code into an if-then-else block, and move the `os.listdir()` loop into the else-block.\n\n```python\nif read_all_files_recursively:\n    for root, directories, files in os.walk(path):\n        for file in files:\n            if file.endswith(file_suffix):\n                log_files.append(os.path.join(root, file))\nelse:\n    for file in os.listdir(path):\n        if file.endswith(file_suffix):\n            log_files.append(os.path.join(path, file))\n\n```\n\n2) Alternatively, do not use `append()` to always add a new list entry, but check if the item exists in the list first.\n\n```python\nfor file in os.listdir(path):\n    if file.endswith(file_suffix):\n        # check if the entry exists in the list already\n        if os.path.isfile(os.path.join(path, file)):\n            log_files.append(os.path.join(path, file))\n\nif read_all_files_recursively:\n    for root, directories, files in os.walk(path):\n        for file in files:\n            if file.endswith(file_suffix):\n                # check if the entry exists in the list already\n                if file not in log_files:\n                    log_files.append(os.path.join(root, file))\n\n```\n\n3) Or, we could eliminate duplicate entries after collecting all items. Python allows converting lists into [sets](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset), which hold unique entries. After applying `set()`, you can again convert the set back into a list. Code Suggestions knows about this possibility, and will help with the comment `# Ensure that only unique file paths are in the list`\n\n![Code Suggestions, converting a list to unique items](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_unique_list.png){: .shadow}\n\n```python\n# Ensure that only unique file paths are in the list\n\nlog_files = list(set(log_files))\n```\n\n4) Take a step back and evaluate whether the variable read_all_files_recursively makes sense. Maybe the default behavior should just be reading all files recursively?\n\n**Tip for testing different paths in VS Code:** Select the code blocks, and press [`cmd /` on macOS](https://code.visualstudio.com/docs/getstarted/keybindings) to comment out the code.\n\n## Functions\n\nLet's create a function called `parse_log_file` that parses a log file, and returns the extracted data. We will define the expected log format and columns to extract, following the [syslog format specification](https://en.wikipedia.org/wiki/Syslog). There are different log format types and also customized formats by developers that need to be taken into account – exercise for later.\n\n### Start with a simple log format\n\nInspect a running Linux VM, or use the following example log file example for additional implementation.\n\n```text\nless /var/log/syslog | grep -v docker\n\nOct 17 00:00:04 ebpf-chaos systemd[1]: Starting Daily dpkg database backup service...\nOct 17 00:00:04 ebpf-chaos systemd[1]: Starting Rotate log files...\nOct 17 00:00:04 ebpf-chaos systemd[1]: dpkg-db-backup.service: Deactivated successfully.\nOct 17 00:00:04 ebpf-chaos systemd[1]: Finished Daily dpkg database backup service.\nOct 17 00:00:04 ebpf-chaos systemd[1]: logrotate.service: Deactivated successfully.\nOct 17 00:00:04 ebpf-chaos systemd[1]: Finished Rotate log files.\nOct 17 00:17:01 ebpf-chaos CRON[727495]: (root) CMD (   cd / && run-parts --report /etc/cron.hourly)\n```\n\nWe can create an algorithm to split each log line by whitespaces, and then join the results again. Let's ask Code Suggestions for help.\n\n```python\n# Split log line \"Oct 17 00:00:04 ebpf-chaos systemd[1]: Finished Rotate log files.\" by whitespaces and save in a list\n\nlog_line = \"Oct 17 00:00:04 ebpf-chaos systemd[1]: Finished Rotate log files.\"\nlog_line_split = log_line.split(\" \")\nprint(log_line_split)\n```\n\nRun the script again to verify the result.\n\n```shell\npython3 log_reader.py\n\n['Oct', '17', '00:00:04', 'ebpf-chaos', 'systemd[1]:', 'Finished', 'Rotate', 'log', 'files.']\n```\n\nThe first three items are part of the datetime string, followed by the host, service, and remaining log message items. Let's practice string operations in Python as the next step.\n\n### String and data structure operations\n\nLet's ask Code Suggestions for help with learning to join strings, and perform list operations.\n\n1. Join the first three items with a whitespace again.\n2. Keep host and service.\n3. Join the remaining variable item count into a string, separated with whitespaces, again.\n4. Store the identified column keys, and their respective values in a new data structure: [dictionary](https://docs.python.org/3/library/stdtypes.html#mapping-types-dict).\n\n![Code suggestions for list items with string operations](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_list_items_string_join_extract.png){: .shadow}\n\n```shell\npython3 log_reader.py\n\n# Array\n['Oct', '17', '00:00:04', 'ebpf-chaos', 'systemd[1]:', 'Finished', 'Rotate', 'log', 'files.']\n\n# Dictionary\n{'datetime': 'Oct 17 00:00:04', 'host': 'ebpf-chaos', 'service': 'systemd[1]:', 'message': ' ebpf-chaos systemd[1]: Finished Rotate log files.'}\n```\n\nA working suggestion can look like the following:\n\n```python\n# Initialize results dictionary with empty values for datetime, host, service, message\n# Loop over log line split\n# Join the first three list items as date string\n# Item 4: host\n# Item 5: service\n# Join the remaining items into a string, separated with whitespaces\n# Print the results after the loop\n\nresults = {'datetime': '', 'host': '', 'service': '', 'message': ''}\n\nfor item in log_line_split:\n\n    if results['datetime'] == '':\n        results['datetime'] = ' '.join(log_line_split[0:3])\n\n    elif results['host'] == '':\n        results['host'] = log_line_split[3]\n\n    elif results['service'] == '':\n        results['service'] = log_line_split[4]\n\n    else:\n        results['message'] += ' ' + item\n\nprint(results)\n```\n\nThe suggested algorithm loops over all log line items, and applies the same operation for the first three items. `log_line_split[0:3]` extracts a slice of three items into a new list. Calling `join()` on a separator character and passing the array as an argument joins the items into a string. The algorithm continues to check for not initialized values for host (Item 4) and service (Item 5)and concludes with the remaining list items appended into the message string. To be honest, I would have used a slightly different algorithm, but it is a great learning curve to see other algorithms, and ways to implement them. Practice with different instructions, and data structures, and continue printing the data sets.\n\n**Tip:** If you need to terminate a script early, you can use `sys.exit()`. The remaining code will not be executed.\n\n```python\nimport sys\nsys.exit(1)\n```\n\nImagine doing these operations for different log formats, and message types – it can get complicated and error-prone very quickly. Maybe there is another approach.\n\n### Parse log files using regular expressions\n\nThere are different syslog format RFCs – [RFC 3164](https://datatracker.ietf.org/doc/html/rfc3164) is obsolete but still found in the wild as default configuration (matching the pattern above), while [RFC 5424](https://datatracker.ietf.org/doc/html/rfc5424) is more modern, including datetime with timezone information. Parsing this format can be tricky, so let's ask Code Suggestions for advice.\n\nIn some cases, the suggestions include regular expressions. They might not match immediately, making the code more complex to debug, with trial and errors. A good standalone resource to text and explain regular expressions is [regex101.com](https://regex101.com/).\n\n**Tip:** You can skip diving deep into regular expressions using the following code snippet as a quick cheat. The next step involves instructing Code Suggestions to use these log patterns, and help us extract all valuable columns.\n\n```python\n# Define the syslog log format regex in a dictionary\n# Add entries for RFC3164, RFC5424\nregex_log_pattern = {\n    'rfc3164': '([A-Z][a-z][a-z]\\s{1,2}\\d{1,2}\\s\\d{2}[:]\\d{2}[:]\\d{2})\\s([\\w][\\w\\d\\.@-]*)\\s(.*)$',\n    'rfc5424': '(?:(\\d{4}[-]\\d{2}[-]\\d{2}[T]\\d{2}[:]\\d{2}[:]\\d{2}(?:\\.\\d{1,6})?(?:[+-]\\d{2}[:]\\d{2}|Z)?)|-)\\s(?:([\\w][\\w\\d\\.@-]*)|-)\\s(.*)$;'\n}\n```\n\nWe know what the function should do, and its input parameters – the file name, and a log pattern to match. The log lines should be split by this regular expression, returning a key-value dictionary for each log line. The function should return a list of dictionaries.\n\n```python\n# Create a function that parses a log file\n# Input parameter: file path\n# Match log line against regex_log_pattern\n# Return the results as dictionary list: log line, pattern, extracted columns\n```\n\n![Code suggestion based on a multiline comment instruction to get a function that parses a log file based on regex patterns](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_log_format_regex_function_instructions_01.png){: .shadow}\n\nRemember the indent for opening a new scope? The same applies for functions in Python. The `def` identifier requires a function name, and a list of parameters, followed by an opening colon. The next lines of code require the indent. VS Code will help with live-linting wrong indent, before the script execution fails, or the CI/CD pipelines.\n\nContinue with Code Suggestions – it might already know that you want to parse all log files, and parse them using the newly created function.\n\n![Code suggestion to parse all log files, and print the result set](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_log_format_regex_function_instructions_02.png){: .shadow}\n\nA full working example can look like this:\n\n```text\nimport os\n\n# Specify the path and file suffix in variables\npath = 'log-data/'\nfile_suffix = '.log'\n\n# Read all file paths in the directory into a list\nlog_files = []\nfor root, directories, files in os.walk(path):\n    for file in files:\n        if file.endswith(file_suffix):\n            log_files.append(os.path.join(root, file))\n\n# Define the syslog log format regex in a dictionary\n# Add entries for RFC3164, RFC5424\nregex_log_pattern = {\n    'rfc3164': '([A-Z][a-z][a-z]\\s{1,2}\\d{1,2}\\s\\d{2}[:]\\d{2}[:]\\d{2})\\s([\\w][\\w\\d\\.@-]*)\\s(.*)$',\n    'rfc5424': '(?:(\\d{4}[-]\\d{2}[-]\\d{2}[T]\\d{2}[:]\\d{2}[:]\\d{2}(?:\\.\\d{1,6})?(?:[+-]\\d{2}[:]\\d{2}|Z)?)|-)\\s(?:([\\w][\\w\\d\\.@-]*)|-)\\s(.*)$;'\n}\n\n# Create a function that parses a log file\n# Input parameter: file path\n# Match log line against regex_log_pattern\n# Return the results as dictionary list: log line, pattern name, extracted columns\nimport re\n\ndef parse_log_file(file_path):\n    # Read the log file\n    with open(file_path, 'r') as f:\n        log_lines = f.readlines()\n\n    # Create a list to store the results\n    results = []\n\n    # Iterate over the log lines\n    for log_line in log_lines:\n        # Match the log line against the regex pattern\n        for pattern_name, pattern in regex_log_pattern.items():\n            match = re.match(pattern, log_line)\n\n            # If the log line matches the pattern, add the results to the list\n            if match:\n                extracted_columns = match.groups()\n                results.append({\n                    'log_line': log_line,\n                    'pattern_name': pattern_name,\n                    'extracted_columns': extracted_columns,\n                    'source_file': file_path\n                })\n\n    # Return the results\n    return results\n\n# Parse all files and print results\nfor log_file in log_files:\n    results = parse_log_file(log_file)\n    print(results)\n\n```\n\nLet's unpack what the `parse_log_file()` function does:\n\n1. Opens the file from `file_path` parameter.\n2. Reads all lines into a new variable `log_lines`.\n3. Creates a results list to store all items.\n4. Iterates over the log lines.\n5. Matches against all regex patterns configured in regex_log_pattern.\n6. If a match is found, extracts the matching column values.\n7. Creates a results item, including the values for the keys `log_line`, `pattern_name`, `extracted_colums`, `source_file`.\n8. Appends the results item to the results list.\n9. Returns the results list.\n\nThere are different variations to this – especially for the returned result data structure. For this specific case, log lines come as list already. Adding a dictionary object instead of a raw log line allows function callers to extract the desired information in the next step. Once a working example has been implemented, you can refactor the code later, too.\n\n### Advanced log format: auth.log\n\nParsing the syslog on a Linux distribution might not unveil the necessary data to analyze. On a virtual machine that exposes port 22 (SSH) to the world, the authentication log is much more interesting – plenty of bots and malicious actors testing default password combinations and often brute force attacks.\n\nThe following snippet from `/var/log/auth.log` on one of my private servers shows the authentication log format and the random attempts from bots using different usernames, etc.\n\n```text\nOct 15 00:00:19 ebpf-chaos sshd[3967944]: Failed password for invalid user ubuntu from 93.254.246.194 port 48840 ssh2\nOct 15 00:00:20 ebpf-chaos sshd[3967916]: Failed password for root from 180.101.88.227 port 44397 ssh2\nOct 15 00:00:21 ebpf-chaos sshd[3967944]: Received disconnect from 93.254.246.194 port 48840:11: Bye Bye [preauth]\nOct 15 00:00:21 ebpf-chaos sshd[3967944]: Disconnected from invalid user ubuntu 93.254.246.194 port 48840 [preauth]\nOct 15 00:00:24 ebpf-chaos sshd[3967916]: Failed password for root from 180.101.88.227 port 44397 ssh2\nOct 15 00:00:25 ebpf-chaos sshd[3967916]: Received disconnect from 180.101.88.227 port 44397:11:  [preauth]\nOct 15 00:00:25 ebpf-chaos sshd[3967916]: Disconnected from authenticating user root 180.101.88.227 port 44397 [preauth]\nOct 15 00:00:25 ebpf-chaos sshd[3967916]: PAM 2 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=180.101.88.227 user=root\nOct 15 00:00:25 ebpf-chaos sshd[3967998]: Invalid user teamspeak from 185.218.20.10 port 33436\n```\n\n**Tip for intrusion prevention:** Add a firewall setup, and use [fail2ban](https://en.wikipedia.org/wiki/Fail2ban) to block invalid auth logins.\n\nThe next exercise is to extend the logic to understand the free form log message parts, for example `Failed password for invalid user ubuntu from 93.254.246.194 port 48840 ssh2`. The task is to store the data in an optional dictionary with key value pairs.\n\nCreate a new function that takes the previously parsed log line results as input, and specifically parses the last list item for each line.\n\n1. Count the number of `Failed password` and `Invalid user` messages.\n2. Return the results with count, log file, pattern\n\n![Code suggestions for a log file message parser to count auth.log failures](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_parse_log_message_auth_log.png){: .shadow}\n\nA working suggestion can look like the following code:\n\n```python\n# Create a function that parses a log file message from the last extracted_columns entry\n# Input: Parsed log lines results list\n# Loop over all log lines in the list, and extract the last list item as message\n# Count failure strings in the message: Failed password, Invalid user\n# Return the results if failure count greater 0: log_file, count, failure string\ndef parse_log_file_message(results):\n    failure_results = []\n\n    # Iterate over the log lines\n    for result in results:\n        # Extract the message from the last list item\n        message = result['extracted_columns'][-1]\n\n        # Count the number of failure strings in the message\n        failure_count = message.count('Failed password') + message.count('Invalid user')\n\n        # If the failure count is greater than 0, add the results to the list\n        if failure_count > 0:\n            failure_results.append({\n                'log_file': result['source_file'],\n                'count': failure_count,\n                'failure_string': message\n            })\n\n    # Return the results\n    return failure_results\n\n# Parse all files and print results\nfor log_file in log_files:\n    results = parse_log_file(log_file)\n    failure_results = parse_log_file_message(results)\n    print(failure_results)\n\n```\n\nThe algorithm follows the previous implementations: First, create a results array to store matching data. Then, iterate over the already parsed log_lines in the list. Each log line contains the `extracted_columns` key, which holds the free-form message string at the end. The next step is to call the string object function `count()` to count how many times a given character sequence is contained in a string. The returned numbers are added up to the `failure_count` variable. If it is greater than zero, the result is added to the results list, including the `log_file`, `count` and `failure_string` key-value pairs. After returning the parsed log message results, loop through all log files, parse them, and print the results again.\n\nExecute the script to inspect the detected matches. Note that the data structure can be optimized in future learning steps.\n\n```shell\npython3 log_reader.py\n\n[{'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3967944]: Failed password for invalid user ubuntu from 93.254.246.194 port 48840 ssh2'}, {'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3967916]: Failed password for root from 180.101.88.227 port 44397 ssh2'}, {'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3967916]: Failed password for root from 180.101.88.227 port 44397 ssh2'}, {'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3967998]: Invalid user teamspeak from 185.218.20.10 port 33436'}, {'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3967998]: Failed password for invalid user teamspeak from 185.218.20.10 port 33436 ssh2'}, {'log_file': 'log-data/var/log/auth.log', 'count': 1, 'failure_string': 'sshd[3968077]: Invalid user mcserver from 218.211.33.146 port 50950'}]\n```\n\n### Parsing more types: Structured logging\n\nApplication developers can use the structured logging format to help machine parsers to extract the key value pairs. Prometheus provides this information in the following structure in syslog:\n\n```text\nOct 17 19:00:10 ebpf-chaos prometheus[594]: ts=2023-10-17T19:00:10.425Z caller=compact.go:519 level=info component=tsdb m\nsg=\"write block\" mint=1697558404661 maxt=1697565600000 ulid=01HCZG4ZX51GTH8H7PVBYDF4N6 duration=148.675854ms\nOct 17 19:00:10 ebpf-chaos prometheus[594]: ts=2023-10-17T19:00:10.464Z caller=head.go:1213 level=info component=tsdb msg\n=\"Head GC completed\" caller=truncateMemory duration=6.845245ms\nOct 17 19:00:10 ebpf-chaos prometheus[594]: ts=2023-10-17T19:00:10.467Z caller=checkpoint.go:100 level=info component=tsd\nb msg=\"Creating checkpoint\" from_segment=2308 to_segment=2309 mint=1697565600000\nOct 17 19:00:10 ebpf-chaos prometheus[594]: ts=2023-10-17T19:00:10.517Z caller=head.go:1185 level=info component=tsdb msg\n=\"WAL checkpoint complete\" first=2308 last=2309 duration=50.052621ms\n```\n\nThis format is easier to parse for scripts, because the message part can be split by whitespaces, and the assignment character `=`. Strings that contain whitespaces are guaranteed to be enclosed with quotes. The downside is that not all programming language libraries provide ready-to-use structured logging libraries, making it harder for developers to adopt this format.\n\nPractice following the previous example to parse the `auth.log` format with additional information. Tell Code Suggestions that you are expecting structured logging format with key-value pairs, and which returned data structure would be great:\n\n```python\n# Create a function that parses a log file message from the last extracted_columns entry\n# Input: Parsed log lines results list\n# Loop over all log lines in the list, and extract the last list item as message\n# Parse structured logging key-value pairs into a dictionary\n# Return results: log_file, dictionary\n```\n\n![Code suggestions for parsing structured logging format in the log file message part](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_propose_structured_logging_message_parser.png){: .shadow}\n\n### Printing results and formatting\n\nMany of the examples used the `print()` statement to print the content on the terminal. Python objects in the standard library support text representation, and for some types it makes more sense (string, numbers), others cannot provide much details (functions, etc.).\n\nYou can also pretty-print almost any data structure (lists, sets, dictionaries) in Python. The JSON library can format data structures in a readable format, and use a given spaces indent to draw the JSON structure on the terminal. Note that we use the `import` statement here to bring libraries into the current scope, and access their methods, for example `json.dumps`.\n\n```python\nimport json\nprint(json.dumps(structured_results, indent=4))\n```\n\n![Parsing log files into structured objects, example result after following the exercises](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_terminal_parsing_logs_and_pretty_print_results.png)\n\nPractice with modifying the existing source code, and replace the code snippets where appropriate. Alternatively, create a new function that implements pretty printing.\n\n```python\n# Create a pretty print function with indent 4\n```\n\n![Code suggestions for pretty-print function](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/vs_code_code_suggestions_pretty_print.png){: .shadow}\n\nThis idea works in a similar fashion with creating your own logger functions...but we have to stop learning and take a break. Before we conclude the first blog post in the learning series, let's ensure that CI/CD and dependencies are set up properly for future exercises and async practice.\n\n## Dependency management and continuous verification\n\n### Pip and pyenv: Bringing structure into Python\n\nDependencies can be managed in the [`requirements.txt` file](https://pip.pypa.io/en/stable/reference/requirements-file-format/), including optional version dependencies. Using `requirements.txt` file also has the advantage of being the single source of truth for local development environments and running continuous builds with GitLab CI/CD. They can use the same installation command:\n\n```shell\npip install -r requirements.txt\n```\n\nSome Linux distributions do not install the pip package manager by default, for example, Ubuntu/Debian require to install the `python3-pip` package.\n\nYou can manage different virtual environments using [venv](https://docs.python.org/3/library/venv.html). This workflow can be beneficial to install Python dependencies into the virtual environment, instead of globally into the OS path which might break on upgrades.\n\n```shell\npip install virtualenv\nvirtualenv venv\nsource venv/bin/activate\n```\n\n### Automation: Configure CI/CD pipeline for Python\n\nThe [CI/CD pipeline](https://docs.gitlab.com/ee/ci/) should continuously lint, test, and build the code. You can mimic the steps from the local development, and add testing more environments and versions:\n\n1. Lint the source code and check for formatting errors. The example uses [Pyflakes](https://pypi.org/project/pyflakes/), a mature linter, and [Ruff](https://docs.astral.sh/ruff/ ), a fast linter written in Rust.\n2. Cache dependencies installed using the pip package manager, following the documentation for [Python caching in GitLab CI/CD](https://docs.gitlab.com/ee/ci/caching/#cache-python-dependencies). This saves time and resources on repeated CI/CD pipeline runs.\n3. Use parallel matrix builds to test different Python versions, based on the available container images on Docker Hub and their tags.\n\n```yaml\nstages:\n  - lint\n  - test\n\ndefault:\n  image: python:latest\n  cache:                      # Pip's cache doesn't store the python packages\n    paths:                    # https://pip.pypa.io/en/stable/topics/caching/\n      - .cache/pip\n  before_script:\n    - python -V               # Print out python version for debugging\n    - pip install virtualenv\n    - virtualenv venv\n    - source venv/bin/activate\n\nvariables:  # Change pip's cache directory to be inside the project directory since we can only cache local items.\n  PIP_CACHE_DIR: \"$CI_PROJECT_DIR/.cache/pip\"\n\n# lint template\n.lint-tmpl:\n  script:\n    - echo \"Linting Python version $VERSION\"\n  parallel:\n    matrix:\n      - VERSION: ['3.9', '3.10', '3.11', '3.12']   # https://hub.docker.com/_/python\n\n# Lint, using Pyflakes: https://pypi.org/project/pyflakes/\nlint-pyflakes:\n  extends: [.lint-tmpl]\n  script:\n    - pip install -r requirements.txt\n    - find . -not -path './venv' -type f -name '*.py' -exec sh -c 'pyflakes {}' \\;\n\n# Lint, using Ruff (Rust): https://docs.astral.sh/ruff/\nlint-ruff:\n  extends: [.lint-tmpl]\n  script:\n    - pip install -r requirements.txt\n    - ruff .\n\n```\n\n![GitLab CI/CD Python lint job view, part of matrix builds](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/gitlab_cicd_python_lint_job_log_view.png)\n\n## What is next\n\nFun fact: GitLab Duo Code Suggestions also helped writing this blog post in VS Code, knowing about the context. In the screenshot, I just wanted to add a tip about [regex101](https://regex101.com/), and GitLab Duo already knew.\n\n![Writing the GitLab blog post in VS Code with support from GitLab Duo Code Suggestions](https://about.gitlab.com/images/blogimages/learn-python-with-ai-code-suggestions-getting-started/gitlab_duo_code_suggestions_helping_write_the_learning_python_ai_blog_post.png)\n\nIn an upcoming blog, we will look into advanced learning examples with more practical (log) filtering and parallel operations, how to fetch logs from API endpoints (CI/CD job logs for example), and more data analytics and observability. Until then, here are a few recommendations for practicing async.\n\n### Async learning exercises\n\n- Implement the missing `log_file_limit` variable check.\n- Print a summary of the results in Markdown, not only JSON format.\n- Extend the script to accept a search filter as environment variable. Print/count only filtered results.\n- Extend the script to accept a date range. It might require parsing the datetime column in a time object to compare the range.\n- Inspect a GitLab CI/CD pipeline job log, and download the raw format. Extend the log parser to parse this specific format, and print a summary.\n\n### Share your feedback\n\nWhich programming language are you learning or considering learning? Start a new topic on our [community](/community/) forum or Discord and share your experience.\n\nWhen you use [GitLab Duo](/gitlab-duo-agent-platform/) Code Suggestions, please share your thoughts and feedback [in the feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/405152).\n",[23,24,25,26],"DevSecOps 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and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[721,722],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[26,277,727,728,729],"google","news","product",{"featured":29,"template":13,"slug":731},"gitlab-and-vertex-ai-on-google-cloud",{"content":733,"config":742},{"heroImage":734,"title":735,"description":736,"authors":737,"date":739,"category":9,"tags":740,"body":741},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[738],"Albert Rabassa","2026-03-05",[9,729,24],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":12,"template":13,"slug":743},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"content":745,"config":755},{"title":746,"description":747,"authors":748,"heroImage":750,"date":751,"body":752,"category":9,"tags":753},"10 AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[749],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[26,754],"DevOps platform",{"featured":12,"template":13,"slug":756},"10-ai-prompts-to-speed-your-teams-software-delivery",{"promotions":758},[759,772,783,795],{"id":760,"categories":761,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[729,569],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":782},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":51,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":818},{"href":55,"dataGaName":56,"dataGaLocation":816},1776447714151]