[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/what-is-a-large-language-model-llm":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Itzik Gan Baruch":688,"blog-related-posts-en-us-what-is-a-large-language-model-llm":702,"assessment-promotions-en-us":742,"next-steps-en-us":780},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/what-is-a-large-language-model-llm.yml","What Is A Large Language Model Llm",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"what-is-a-large-language-model-llm",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"What is a large language model (LLM)?","Learn how large language models work, their applications, and their impact on the DevSecOps world.",[18],"Itzik Gan Baruch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660057/Blog/Hero%20Images/LLM.jpg","2025-05-29","Large language models (LLMs) are revolutionizing DevOps and DevSecOps approaches by simplifying complex tasks, such as code creation, log analysis, and vulnerability detection.\n\nIn this article, you will learn how LLMs work, their practical applications, and the main challenges to overcome in order to fully harness their potential.\n\n## What is an LLM?\n\nLLMs are artificial intelligence (AI) systems that can process and generate text autonomously. They are trained by analyzing vast amounts of data from a variety of sources, enabling them to master the linguistic structures, contextual relationships, and nuances of language.\n\nLLMs are a major breakthrough in the field of AI. Their ability to process, generate, and interpret text relies on sophisticated machine learning and natural language processing (NLP) techniques. These systems do not just process individual words; they analyze complex sequences to capture the overall meaning, subtle contexts, and linguistic nuances.\n\n## How do LLMs work?\n\nTo better understand how they work, let's explore some of the key features of large language models.\n\n### Supervised and unsupervised learning\n\nLLMs are trained using two complementary approaches: supervised learning and unsupervised learning. These two approaches to machine learning maximize their ability to analyze and generate text.\n\n* **Supervised learning** relies on labeled data, where each input is associated with an expected output. The model learns to associate these inputs with the correct outputs by adjusting its internal parameters to reduce prediction errors. Through this approach, the model acquires precise knowledge about specific tasks, such as text classification or named entity recognition.\n\n* **Unsupervised learning (or machine learning)**, on the other hand, does not require labeled data. The model explores large volumes of text to discover hidden structures and identify semantic relationships. The model is therefore able to learn recurring patterns, implicit grammatical rules in the text, and contextualization of sentences and concepts. This method allows LLMs to be trained on large corpora of data, greatly accelerating their progress without direct human action.\n\nBy combining these two approaches, large language models gain the advantages of both precise, human-guided learning and unlimited autonomous exploration. This complementarity allows them to develop rapidly, while continuously improving their ability to understand and generate text coherently and contextually.\n\n### Learning based on a large volume of data\n\nLLMs are trained on billions of sentences from a variety of sources, such as news articles, online forums, technical documentation, scientific studies, and more. This variety of sources allows them to acquire a broad and nuanced understanding of natural language, ranging from everyday expressions to specialized terminology.\n\nThe richness of the data used is a key factor in LLMs' performance. Each source brings different writing styles, cultural contexts, and levels of technicality. \n\nFor example:\n\n* **News articles** to master informative and factual language \n* **Online forums** to understand specialized communities' informal conversations and technical language  \n* **Technical documentation and scientific studies** to assimilate complex concepts and specific terminology, particularly in areas such as DevOps and DevSecOps\n\nThis diversity of content allows LLMs to recognize complex linguistic structures, interpret sentences in different contexts, and adapt to highly technical domains. In DevSecOps, this means understanding commands, configurations, security protocols, and even concepts related to the development and maintenance of computer systems.\n\nWith this large-scale training, LLMs can accurately answer complex questions, write technical documentation, or identify vulnerabilities in computer systems.\n\n### Neural network architecture and \"deep learning\"\n\nLLMs are based on advanced neural network architectures. These networks are specially designed to process large sequences of text while maintaining an accurate understanding of the context. This deep learning-based training is a major asset in the field of NLP.\n\nThe best-known of these structures is the architecture of sequence-to-sequence models (transformers). This architecture has revolutionized NLP with its ability to simultaneously analyze all parts of a text, unlike sequential approaches that process words one by one.\n\nSequence-to-sequence models excel at processing long texts. For example, in a conversation or a detailed technical document, they are able to link distant information in the text to produce precise and well-reasoned answers. This context management is essential in a DevSecOps approach, where instructions can be complex and spread over multiple lines of code or configuration steps.\n\n### Predictive text generation\n\nWhen the user submits a text, query, or question, an LLM uses its predictive ability to generate the most likely sequence, based on the context provided.\n\nThe model analyzes each word, studies grammatical and semantic relationships, and then selects the most suitable terms to produce a coherent and informative text. This approach makes it possible to generate precise, detailed responses adapted to the expected tone.\n\nIn DevSecOps environments, this capability becomes particularly useful for:\n\n* **Coding assistance:** generation of code blocks or scripts adapted to specific configurations\n* **Technical problem solving:** proposing solutions based on descriptions of bugs or errors\n* **Drafting technical documentation:** automatic creation of guides, manuals, or instructions\n\nPredictive text generation thus makes it possible to automate many repetitive tasks and speed up technical teams' work.\n\n## Applications of large language models in a DevSecOps approach\n\nWith the rise of automation, LLMs have become indispensable allies for technical teams. Their ability to understand and generate text contextually enables them to effectively operate in complex environments such as [DevSecOps](https://about.gitlab.com/topics/devsecops/).\n\nWith their analytical power and ability to adapt to specific needs, these models offer tailored solutions to streamline processes and lighten technical teams' workload.\n\nDevelopment teams can leverage LLMs to automatically transform functional specifications into source code. \n\nWith this capability, they can perform the following actions:\n- generate complex automation scripts\n- create CI/CD pipelines tailored to specific business processes\n- produce customized security patches\n- generate code explanation and create documentation\n- refactor code by improving code structure and readability without changing functionality\n- generate tests\n\nBy relying on LLMs, teams are able to accelerate the development of their software while reducing the risk of human error.\n\n### Improved documentation and knowledge sharing\n\nThese powerful tools make it easy to create customized user manuals, API descriptions, and tutorials that are perfectly tailored to each user's level of expertise. By leveraging existing knowledge bases, LLMs create contextual answers to frequently asked questions. This enhances knowledge sharing within teams, speeds up onboarding of new members, and helps centralize best practices.\n\n### Incident management and troubleshooting\nDuring an incident, LLMs play a crucial role in analyzing logs and [trace files](https://docs.gitlab.com/ee/development/tracing.html) in real time. Thanks to their ability to cross-reference information from multiple sources, they identify anomalies and propose solutions based on similar past incidents. This approach significantly reduces diagnosis time. In addition, LLMs can automate the creation of detailed incident reports and recommend specific corrective actions.\n\n### Creating and improving CI/CD pipelines\n\nLLMs are revolutionizing the configuration of [CI/CD pipelines](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/). They can not only help create pipelines, but also automate this process and suggest optimal configurations based on industry standards. By adapting workflows to your specific needs, they ensure perfect consistency between different development environments. Automated testing is enhanced by relevant suggestions, limiting the risk of failure. LLMs also continuously monitor the efficiency of pipelines and adjust processes to ensure smooth and uninterrupted rollout.\n\n### Security and compliance\n\nIn a DevSecOps environment, large language models become valuable allies for security and compliance. They parse the source code for potential vulnerabilities and generate detailed patch recommendations. LLMs can also monitor the application of security standards in real time, produce comprehensive compliance reports, and automate the application of security patches as soon as a vulnerability is identified. This automation enhances overall security and ensures consistent compliance with legal and industry requirements.\n\n## What are the benefits of large language models?\n\nLLMs are radically reshaping DevOps and DevSecOps approaches, bringing substantial improvements in productivity, security, and software quality. By integrating with existing workflows, LLMs are disrupting traditional approaches by automating complex tasks and providing innovative solutions.\n\n### Improved productivity and efficiency\n\nLLMs play a central role in improving technical teams' productivity and efficiency. By automating a wide range of repetitive tasks, they free development teams from routine operations, allowing them to focus on strategic activities with higher added value.\n\nIn addition, LLMs act as intelligent technical assistants capable of instantly providing relevant code snippets, tailored to the specific context of each project. In this way, they significantly reduce research time by offering ready-to-use solutions to assist teams in their work. This targeted assistance speeds up problem solving and reduces disruptions in workflows.\nAs a result, productivity increases and projects move forward more quickly. Technical teams can take on more tasks without compromising the quality of deliverables.\n\n### Improved code quality and security\n\nThe use of large language models in software development is a major lever for improving both code quality and application security. With their advanced analytical capabilities, LLMs can scan source code line by line and instantly detect syntax errors, logical inconsistencies, and potential vulnerabilities. Their ability to recognize defective code allows them to recommend appropriate fixes that comply with industry best practices.\n\nLLMs also play a key preventive role. They excel at identifying complex security flaws that are often difficult for humans to detect. By analyzing dependencies, they can flag obsolete or vulnerable libraries and recommend more secure, up-to-date versions. This approach contributes to maintaining a secure environment that complies with current security standards.\n\nBeyond fixing existing errors, LLMs offer improvements by suggesting optimized coding practices and project structures. They can generate code that meets the most advanced security standards from the earliest stages of development.\n\n### Accelerating development lifecycles\n\nLarge language models play a key role in accelerating software development lifecycles by automating key tasks that would otherwise tie up valuable human resources. Complex and repetitive tasks, such as writing functions, creating unit tests, or implementing standard components, are automated in a matter of moments.\n\nLLMs also speed up the validation phase with their ability to suggest complete and appropriate test cases. They ensure broader test coverage in less time, reducing the risk of errors and enabling early detection of anomalies. This preventive approach shortens the correction cycle and limits delays related to code quality issues.\n\nBy simplifying technical tasks and providing fast and tailored solutions, large language models enable businesses to respond to market demands in a more agile way. This acceleration of the development lifecycle results in more frequent updates, faster iterations, and a better ability to adapt products to users' changing needs.\n\nDevelopment lifecycles are becoming shorter, providing a critical strategic advantage in an increasingly demanding technology landscape.\n\n## What are the challenges of using LLMs?\n\nDespite their many benefits, large language models have certain limitations that require careful management. Their effectiveness depends heavily on the quality of the data used during their training and regular updates to their knowledge bases. In addition, issues related to algorithmic bias, data security, and privacy can arise, exposing companies to operational and legal risks. Rigorous human oversight remains essential in order to ensure the reliability of results, maintain regulatory compliance, and prevent critical errors.\n\n### Data privacy and security\n\nTraining LLMs relies on large volumes of data, often from diverse sources, raising questions about the protection of confidential information. Sensitive data shared with cloud platforms can therefore be exposed to potential breaches. This is of particular concern to companies operating in regulated sectors. \n\nIn Europe, where strict regulations like GDPR govern data management, many companies are reluctant to transfer their information to external services. Regulatory requirements, coupled with the fear of unauthorized exploitation of sensitive data, have led some companies to opt for self-hosted solutions to maintain complete control over their systems.\n\nProviders like GitLab have put in place robust security guarantees, such as intentional non-retention of personal data and end-to-end encryption. However, this may not be enough for the most demanding customers, who prefer complete control of their environments. Implementing hybrid or on-premises solutions then becomes a strategic necessity to meet the security requirements of certain companies.\n\nLearn more about GitLab Duo Self-Hosted by clicking on the image below to access our product tour.\n\n[![GitLab Duo Self-Hosted tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673815/Blog/Content%20Images/Screenshot_2025-05-29_at_8.29.30%C3%A2__AM.png)](https://gitlab.navattic.com/gitlab-duo-self-hosted)\n\n### Accuracy and reliability\n\nAlthough large language models are capable of producing impressive results, their performance is not infallible. They can produce incorrect, incomplete, or inconsistent answers. This inaccuracy becomes particularly problematic in the context of critical tasks such as generating security code or analyzing sensitive data.\n\nIn addition, LLMs operate on the basis of probabilistic models, which means that they do not truly \"understand\" the content they process, but produce predictions based on statistical probabilities. This can lead to technically incorrect or even dangerous recommendations when used without human validation.\n\nTo avoid these pitfalls, it is essential to maintain constant oversight and establish rigorous validation processes. The results provided by LLMs must always be reviewed by humans before being integrated into critical systems.\n\nA strategy of regular model updates, combined with proactive human oversight, can reduce errors and gradually improve the reliability of results.\n\n## How GitLab uses LLMs for GitLab Duo features\n\n[GitLab Duo](https://about.gitlab.com/gitlab-duo/) harnesses the power of large language models to transform DevSecOps processes by integrating AI-powered capabilities throughout the software development lifecycle. This approach aims to improve productivity, strengthen security, and automate complex tasks so that development teams can focus on high added-value tasks.\n\n### AI-assisted software development\n\nGitLab Duo provides continuous support throughout the software development lifecycle with real-time recommendations. Development teams can automatically generate unit tests, get detailed explanations of complex code segments, and benefit from suggestions to improve the quality of their code.\n\n### Proactive CI/CD failure analysis\n\nOne of the key features of GitLab Duo is its assistance in analyzing CI/CD job failures. With LLM and AI, teams are able to quickly identify sources of errors in their continuous integration and deployment pipelines. \n\n### Enhanced code security\n\nGitLab Duo incorporates AI-based security features. The system detects vulnerabilities in the source code and proposes detailed patches to reduce the risks. Teams receive clear explanations of the nature of the vulnerabilities identified and can apply automated patches via [merge requests](https://docs.gitlab.com/ee/user/project/merge_requests/) generated directly by GitLab Duo. This feature helps secure development without slowing down development lifecycles.\n\nLearn more about GitLab Duo Vulnerability Explanation and Resolution by clicking on the image below to access our product tour.\n\n[![Vulnerability report interactive tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673816/Blog/Content%20Images/Screenshot_2025-05-29_at_8.32.15%C3%A2__AM.png)](https://gitlab.navattic.com/ve-vr-short)\n\n### Key features of GitLab Duo\n\n* [GitLab Duo Chat](https://about.gitlab.com/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat/): This conversational feature processes and generates text and code intuitively. It allows users to quickly search for relevant information in large volumes of text, including in tickets, [epics](https://docs.gitlab.com/ee/user/group/epics/), source code, and [GitLab documentation](https://docs.gitlab.com/).\n\n* [GitLab Duo Self-Hosted](https://about.gitlab.com/blog/gitlab-duo-self-hosted-enterprise-ai-built-for-data-privacy/): GitLab Duo Self-Hosted allows companies with strict data privacy requirements to benefit from GitLab Duo's AI capabilities with flexibility in choosing deployment and LLMs from a list of supported options.\n\n* [GitLab Duo Code Suggestions](https://about.gitlab.com/direction/create/code_creation/code_suggestions/): Development teams benefit from automated code suggestions, allowing them to write secure code faster. Repetitive and routine coding tasks are automated, significantly speeding up software development lifecycles.\n\nGitLab Duo is not limited to these features. 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Whether it's automating testing, improving collaboration between teams, or strengthening project security, GitLab Duo is a complete solution for smart and efficient DevSecOps processes.\n\nLearn more about GitLab Duo Enterprise by clicking on the image below to access our product tour. \n\n[![GitLab Duo Enterprise interactive 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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.",[708],"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.",[23,713],"DevOps platform",{"featured":12,"template":13,"slug":715},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":717,"config":727},{"title":718,"description":719,"heroImage":720,"authors":721,"date":723,"body":724,"category":9,"tags":725},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[722],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[23,726],"security",{"featured":27,"template":13,"slug":728},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"category":9,"tags":735,"date":737,"heroImage":738,"body":739},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[734],"Regnard Raquedan",[23,736,107,24],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":12,"template":13,"slug":741},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":743},[744,757,769],{"id":745,"categories":746,"header":747,"text":748,"button":749,"image":754},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":750,"config":751},"Get your AI maturity score",{"href":752,"dataGaName":753,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":755},{"src":756},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":758,"categories":759,"header":761,"text":748,"button":762,"image":766},"devops-modernization",[760,36],"product","Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":753,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":772,"text":748,"button":773,"image":777},"security-modernization",[726],"Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":753,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":781,"blurb":782,"button":783,"secondaryButton":788},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":784,"config":785},"Get your free trial",{"href":786,"dataGaName":49,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":789},{"href":53,"dataGaName":54,"dataGaLocation":787},1772652087254]