RAG (Retrieval-Augmented Generation): Definition and How It Works

RAG is one of the technical building blocks powering today's autonomous AI agents, alongside tool calling and persistent memory. The acronym comes from a research paper published in May 2020 by a Facebook AI Research team (now Meta), led by Patrick Lewis. At the time, the observation was simple: large language models store an impressive amount of knowledge in their parameters, but they can neither update it easily nor point precisely to a source. RAG offered a solution: let the model retrieve information from an external base at answer time, rather than relying entirely on its memory frozen at training.

Detailed definition

Retrieval-Augmented Generation refers to an architecture that combines two distinct building blocks: a retrieval mechanism that fetches relevant passages from an external document base, and a text-generation model that drafts the final response from those passages. In the founding paper by Lewis and co-authors (NeurIPS 2020), RAG pairs a parametric memory (the pre-trained model's internal knowledge) with a non-parametric memory (a document base consulted on the fly), to improve performance on tasks requiring precise knowledge.

Consensus around this definition is broad: whether in academic literature or in mainstream explainers like NVIDIA's, RAG always comes back to the same two-step mechanic, retrieve then generate. Disagreements mostly concern the technical implementation (type of vector database, document-chunking method, number of passages retrieved), not the definition of the principle itself.

RAG addresses two well-known limitations of language models: hallucinations, when the model invents a plausible but false answer, and staleness, when information has changed since the model's training cutoff. By grounding generation in real, up-to-date documents, RAG reduces both problems without requiring the model to be retrained.

How it works

A typical RAG request follows three steps. First, the user's question (or an agent's need) is turned into a search against a document base, often via similarity search over vector representations of the text, sometimes combined with classic keyword search. Next, the most relevant passages returned by that search are injected into the context sent to the language model, alongside the original question. Finally, the model generates its response drawing on those passages, ideally citing or aligning with their content rather than improvising.

This mechanic lends itself well to use by an autonomous AI agent: document search can be exposed as a tool the agent calls itself, whenever it judges it needs to, rather than being systematically triggered before every response. That's the difference between "static" RAG, wired in ahead of every request, and "agentic" RAG, where the model decides when to search, what to search for, and whether to search again if the first result isn't enough.

A concrete example from Atako

Atako's public documentation doesn't detail the internal document-search architecture used by its agents, so it can't be stated here that Atako uses RAG in the strict sense of Lewis et al.'s paper. What is documented, however, is the closest mechanism: every agent has access to a file library shared by the company, organized into folders, with previewable formats (Markdown, CSV, JSON, PDF, HTML, images, text, source code). These files can be shared with an agent as read access, or added directly to a folder dedicated to the agent, either by a human or by the agent itself.

This file library, combined with an agent's persistent memory that survives restarts and pauses, plays the role of an external knowledge base the agent can consult before answering or acting, which matches the spirit of the RAG principle: retrieving real information rather than relying solely on what the model memorized during training. An agent configured for customer support, for example, can consult product documentation files shared in its folder before answering a ticket, rather than guessing the answer.

Common mistakes

A first common confusion: believing RAG is a feature you switch on with a click, like a button in a piece of software. It's actually an architecture, with concrete technical choices (how to chunk documents, how to index them, how many passages to retrieve) that directly determine the quality of the answers.

Second mistake: thinking RAG eliminates hallucinations entirely. It reduces them by grounding generation in verifiable documents, but a model can still misinterpret a passage, or generate a response from irrelevant documents returned by an imperfect search. RAG shifts the risk, it doesn't remove it.

Third mistake: confusing RAG with fine-tuning. Fine-tuning permanently changes the model by retraining it on specific data; RAG leaves the model untouched and adds an external base consulted on every request, which is faster to update and generally cheaper.

Finally, underestimating the importance of document-base quality is a classic mistake. A RAG system wired to outdated, poorly structured, or incomplete documents will produce poor-quality answers, no matter how powerful the underlying generation model is.