Everything you need to know about Retrieval-Augmented Generation (RAG)

Article updated on 09/10/26

The role of AI in IT Service Management

Retrieval-Augmented Generation (RAG) is a technique that connects AI models to external knowledge sources so they can retrieve relevant, up-to-date information before generating a response. Alongside Large Language Models (LLMs) and Knowledge Graphs (KGs), RAG is reshaping how IT organizations manage and act on vast amounts of operational data.

Understanding how these technologies interact reveals their potential to transform ITSM. LLMs are advanced AI models trained on massive datasets to generate human-like text based on the input they receive. But LLMs have a critical limitation: they lack memory and have no access to real-time information. They can also lose focus and hallucinate, especially when processing large or complex inputs.

To address these limitations, Retrieval-Augmented Generation (RAG) plays a critical role. RAG enhances LLMs by dynamically retrieving external information from a knowledge base at the time of the query, then using that information to generate more accurate and relevant responses.

The process works in two stages. In the retrieval stage, when a user submits a query, the system searches a designated data repository—such as a vector database or internal knowledge store—for documents related to the query. In the generation stage, the system passes both the retrieved documents and the original query to the LLM, which synthesizes a response grounded in the provided facts rather than relying solely on its training data.

While RAG significantly enhances LLMs by providing access to external data, Knowledge Graphs (KGs) offer another layer of sophistication. KGs are structured databases that store data in an interconnected network of entities and their relationships. They provide a structured way to represent knowledge in various domains, including ITSM.

KGs can further enhance LLM performance where RAG might still fall short—especially in complex, multi-step problem-solving scenarios common in ITSM. By navigating through connected data points, systems can extract and utilize information that is contextually relevant to the user’s specific needs.

Together, LLMs, RAG, and KGs form a strong combination for IT Service Management use cases:

• LLMs provide powerful language understanding and generation capabilities.

• RAG augments those models with dynamic, real-time information retrieval.

• KGs deliver deep, structured contextual insights across interconnected data.

When combined within an ITSM platform, these technologies can drive meaningful gains in automation, accuracy, and efficiency. Here’s how they work in practice—and where they’re headed.

How LLMs, RAG, and knowledge graphs work together in ITSM

This image provides a simplified, hypothetical example of how Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Knowledge Graphs (KGs) can work together to enhance IT Service Management (ITSM).

The system extracts key information from a knowledge base and maps it onto a Knowledge Graph, which illustrates how various elements like the server, application, and related devices are interconnected.

This structured representation is stored in a vector database—a storage system that saves data as numerical representations (vectors), enabling high-speed searches based on meaning rather than just keyword matching. An embedding model converts both the knowledge base content and the user’s query into this vector format, preserving the semantic relationships between different pieces of information.

This format allows the system to search the Knowledge Graph and related databases for relevant context. The LLM then uses this context to generate a coherent and precise response.

This approach demonstrates how these technologies can complement each other: the Knowledge Graph provides structured context, RAG dynamically retrieves up-to-date data, and the LLM synthesizes this information into a useful, actionable insight.

One important caveat: the effectiveness of a RAG system depends on the quality of the data it retrieves. If knowledge base content is outdated, incomplete, or poorly structured, the LLM will generate responses based on flawed inputs. In ITSM, this makes data governance and knowledge management discipline a prerequisite—not an afterthought—for any AI-augmented workflow.

Applying RAG, LLMs, and knowledge graphs in ITSM

The integration of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Knowledge Graphs (KGs) is already reshaping the IT landscape. Together, these technologies enhance IT Operations Management, IT Service Management, and Artificial Intelligence for IT Operations (AIOps) in ways that go well beyond basic automation.

Implementing LLMs within ITSM frameworks enables instantaneous, context-aware responses to user inquiries—reducing resolution times and improving satisfaction. Specific use cases include:

• Automating ticket generation and categorization, reducing manual triage effort.

• Sentiment analysis, prioritizing issues based on urgency to meet Service Level Agreement targets more consistently.

• Virtual assistants and chatbots that summarize interactions and surface relevant knowledge, enhancing operational efficiency across ITSM workflows.

RAG strengthens this further by improving the retrieval of pertinent information from expansive knowledge bases. Rather than allowing the model to generate plausible but potentially incorrect answers, RAG forces the system to verify information against trusted sources before responding—helping support teams identify and apply the most relevant solutions faster.

Knowledge Graphs augment decision-making by providing structured visualizations of relationships among IT assets, incidents, and solutions. This clarity helps teams navigate complex scenarios, make more informed decisions, and simplify the identification of recurring incidents.

Beyond customer support, LLMs, RAG, and KGs enhance other essential IT functions. They refine recommender systems by delivering precise, context-sensitive suggestions based on both historical and real-time data analysis.

In the domain of AIOps, these technologies play a growing role in failure management—analyzing logs, pinpointing root causes, and automating corrective actions to minimize downtime and improve system reliability. There’s also a governance advantage: RAG allows organizations to keep proprietary data in secure, internal environments, since the model retrieves information from a private knowledge base rather than requiring sensitive data to be sent externally for retraining.

For organizations navigating rising service demands and tightening compliance requirements, these capabilities represent a meaningful step toward more resilient, AI-augmented IT operations.

Domain-specific and task-specific LLMs for ITSM

While general-purpose Large Language Models (LLMs) have proven effective across a wide range of applications, they often fall short in specialized domains like IT Service Management (ITSM). These models are trained on vast, diverse datasets that rarely include the deep, domain-specific knowledge needed to navigate the unique challenges of ITSM. The result: less accurate responses, technical misinterpretations, or an incomplete understanding of IT operations and protocols.

In contrast, domain-specific and task-specific LLMs can offer a significant advantage in ITSM applications. These models can be fine-tuned on datasets that are rich in ITSM-specific language and scenarios, enabling them to better understand and respond to the needs of the domain. For instance, a model trained specifically for ITSM is likely to better handle tasks like incident categorization and problem resolution.

Integrating these models with technologies like Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) can further enhance their effectiveness. Which can help in managing complex, multi-hop question-and- answer scenarios, where an answer requires combining information from multiple sources effectively.

Additionally, semantic search using embeddings, which match user queries to the most relevant information, can sometimes miss the user’s true intent. For example, if a user submits a ticket asking for help with a “server outage” but specifies “not related to network issues,” the system might still return network-focused troubleshooting steps. This is precisely the kind of gap that domain-specific models, enhanced by knowledge graphs, are well-suited to close.

These tailored LLMs, especially when enhanced with KGs and domain-specific embedding models, represent a promising future for AI in ITSM. At our AI lab, we are committed to pushing the boundaries of what’s possible in IT Service Management through advanced AI solutions.

We are currently focused on fine-tuning LLMs with robust multilingual capabilities specifically adapted to ITSM use cases, alongside multilingual embedding models designed to integrate seamlessly into RAG pipelines, search functionalities, and Knowledge Graph embeddings. This ensures our models handle diverse linguistic requirements while remaining deeply connected to ITSM processes.

By combining the strengths of LLMs with cutting-edge RAG techniques and Knowledge Graphs, we are enhancing the knowledge base and response accuracy of our AI solutions. Looking ahead, we see significant potential in emerging RAG approaches including multimodal RAG, agentic RAG, and graph RAG which will further enhance AI’s ability to understand context, orchestrate multi-step workflows, and generate meaningful responses in IT environments.

If you’re evaluating how AI can strengthen your ITSM foundation without adding complexity, our ongoing research may offer a useful starting point.

Learn more by downloading our latest EV Pulse AI datasheet or schedule a demo to see these capabilities in action.

Frequently asked questions

#1 What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that helps AI models give more accurate, up-to-date answers by connecting them to an external knowledge base at the moment a question is asked. Standard large language models rely only on their training data, which can become outdated or lead to fabricated responses, a problem known as hallucination. RAG addresses this directly. Instead of guessing from memory, the model first retrieves relevant, current information and then uses it to build its response. The result is an answer grounded in real data, not just patterns learned during training.

#2 How does RAG work?

RAG works in two connected stages. In the retrieval stage, the system takes a user’s question and searches an external knowledge base for relevant documents or data. It does this by converting both the query and stored content into numerical representations called embeddings, which allows it to match meaning rather than just keywords. In the generation stage, the model receives both the retrieved information and the original question, then uses them together to form a response. This keeps answers accurate and directly tied to real, current information rather than the model’s static training data.

#3 What is RAG used for?

RAG is used wherever AI responses need to be accurate, current, and backed by real information. Common use cases include customer support chatbots, internal knowledge assistants, compliance Q&A, and document search. In IT Service Management, RAG is particularly valuable: support teams can surface the right solution from large, constantly updated knowledge bases in seconds, reducing resolution times and the risk of incorrect guidance. Any environment where outdated or fabricated AI responses create operational or reputational risk is a strong candidate for a RAG-based approach.

#4 Is ChatGPT a RAG model?

ChatGPT is a large language model trained on broad datasets, it is not a RAG system by default. Some versions include retrieval-like features, such as web browsing or the ability to process uploaded documents, but the base model does not use RAG architecture. The distinction matters. RAG is a design approach, not a product. Organizations implement it by connecting an LLM to their own curated knowledge base, which gives them direct control over the data the AI draws from. That level of control is especially important in ITSM, where accuracy and data governance are non-negotiable.