Large language models (LLMs) are revolutionizing DevOps and DevSecOps approaches by simplifying complex tasks, such as code creation, log analysis, and vulnerability detection.
In this article, you will learn how LLMs work, their practical applications, and the main challenges to overcome in order to fully harness their potential.
What is an LLM?
LLMs 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.
LLMs 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.
How do LLMs work?
To better understand how they work, let's explore some of the key features of large language models.
Supervised and unsupervised learning
LLMs 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.
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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.
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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.
By 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.
Learning based on a large volume of data
LLMs 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.
The richness of the data used is a key factor in LLMs' performance. Each source brings different writing styles, cultural contexts, and levels of technicality.
For example:
- News articles to master informative and factual language
- Online forums to understand specialized communities' informal conversations and technical language
- Technical documentation and scientific studies to assimilate complex concepts and specific terminology, particularly in areas such as DevOps and DevSecOps
This 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.
With this large-scale training, LLMs can accurately answer complex questions, write technical documentation, or identify vulnerabilities in computer systems.
Neural network architecture and "deep learning"
LLMs 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.
The 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.
Sequence-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.
Predictive text generation
When 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.
The 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.
In DevSecOps environments, this capability becomes particularly useful for:
- Coding assistance: generation of code blocks or scripts adapted to specific configurations
- Technical problem solving: proposing solutions based on descriptions of bugs or errors
- Drafting technical documentation: automatic creation of guides, manuals, or instructions
Predictive text generation thus makes it possible to automate many repetitive tasks and speed up technical teams' work.
Applications of large language models in a DevSecOps approach
With 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.
With their analytical power and ability to adapt to specific needs, these models offer tailored solutions to streamline processes and lighten technical teams' workload.
Development teams can leverage LLMs to automatically transform functional specifications into source code.
With this capability, they can perform the following actions:
- generate complex automation scripts
- create CI/CD pipelines tailored to specific business processes
- produce customized security patches
- generate code explanation and create documentation
- refactor code by improving code structure and readability without changing functionality
- generate tests
By relying on LLMs, teams are able to accelerate the development of their software while reducing the risk of human error.
Improved documentation and knowledge sharing
These 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.
Incident management and troubleshooting
During an incident, LLMs play a crucial role in analyzing logs and trace files 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.
Creating and improving CI/CD pipelines
LLMs are revolutionizing the configuration of CI/CD pipelines. 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.
Security and compliance
In 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.
What are the benefits of large language models?
LLMs 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.
Improved productivity and efficiency
LLMs 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.
In 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. As a result, productivity increases and projects move forward more quickly. Technical teams can take on more tasks without compromising the quality of deliverables.
Improved code quality and security
The 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.
LLMs 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.
Beyond 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.
Accelerating development lifecycles
Large 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.
LLMs 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.
By 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.
Development lifecycles are becoming shorter, providing a critical strategic advantage in an increasingly demanding technology landscape.
What are the challenges of using LLMs?
Despite 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.
Data privacy and security
Training 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.
In 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.
Providers 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.
Learn more about GitLab Duo Self-Hosted by clicking on the image below to access our product tour.
Accuracy and reliability
Although 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.
In 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.
To 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.
A strategy of regular model updates, combined with proactive human oversight, can reduce errors and gradually improve the reliability of results.
How GitLab uses LLMs for GitLab Duo features
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.
AI-assisted software development
GitLab 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.
Proactive CI/CD failure analysis
One 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.
Enhanced code security
GitLab 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 generated directly by GitLab Duo. This feature helps secure development without slowing down development lifecycles.
Learn more about GitLab Duo Vulnerability Explanation and Resolution by clicking on the image below to access our product tour.
Key features of GitLab Duo
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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, source code, and GitLab documentation.
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GitLab Duo Self-Hosted: 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.
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GitLab Duo 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.
GitLab Duo is not limited to these features. It offers a wide range of features designed to simplify and optimize software development. 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.
Learn more about GitLab Duo Enterprise by clicking on the image below to access our product tour.
