What is a large language model (LLM)?

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Published on: May 29, 2025
Learn how large language models work, their applications, and their impact on the DevSecOps world.
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.
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.
To better understand how they work, let's explore some of the key features of large language models.
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.
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.
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.
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.
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.
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:
Predictive text generation thus makes it possible to automate many repetitive tasks and speed up technical teams' work.
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:
By relying on LLMs, teams are able to accelerate the development of their software while reducing the risk of human error.
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.
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.
LLMs are revolutionizing the configuration of CI/CD pipelines. They can not only help create pipelines, but also