Retrieval Augmented Generation

    What is RAG and how can it be used? What advantages does it offer? Find out more in this article.

    What is RAG?

    Retrieval-Augmented Generation (RAG) is an AI approach that combines generative models with an external knowledge database to provide more informed answers. Put simply, before an answer is generated, relevant knowledge is first retrieved to provide additional context. This information can come from company documents, knowledge databases or the web. The model (such as an LLM) then generates the output, incorporating the retrieved facts. The aim of RAG is to combine the strengths of search methods (timeliness, factual accuracy) with the strengths of generative AI (fluency, contextual understanding).

    How it works

    The RAG framework typically consists of two components:

    • Retriever: A search or retrieval module (e.g. a semantic search, vector database) that finds relevant documents or text snippets from a defined knowledge base based on user input. Example: The user question "What are the core functions of guardrails in LLMs?" prompts the retriever to search a collection of AI articles for the sections in which guardrails for LLMs are described.

    • Generator: The generative model (usually a language model), which now receives both the original question and the additional information retrieved by the retriever. It "grounds" its answer in this information – that is, it bases the formulation directly on the sources. For example, it can quote a specific passage or include technically correct details.

    By splitting the data, the system can also use current or organization-specific data that was not included in the data originally learned with the LLM. The LLM does not need to be retrained; it is provided with up-to-date knowledge on the fly.

    Advantages

    • Up-to-date: LLMs often have a knowledge cut-off date (e.g. knowledge until 2021). With RAG, you can, for example, provide current changes to the law or news as context so that the model can provide information on this.

    • Factual accuracy: As the model uses concrete sources, the risk of hallucinations is reduced. It knows where the information comes from and can even generate quotes from it.

    • Domain knowledge: Companies can link their internal documentation or manuals as a knowledge base. The generative model then answers employee questions with reference to this secure information – a kind of intelligent internal company assistant.

    Challenges

    RAG systems are more complex because they require a reliable search. The retriever must be relevant and precise; if it returns incorrect or irrelevant sections, the generated answer can also go wrong. In addition, the generative model must learn to use the given context correctly and not ignore it. Nevertheless, RAG has proven to be a very effective approach to make LLM applications more practical and reliable – you get the best of both worlds.

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