Retrieval-Augmented Generation (RAG)
Last updated: February 16, 2026
Retrieval-augmented generation (RAG) is a technique that enhances an LLM's responses by first retrieving relevant information from an external knowledge base, then including that information in the prompt before the model generates its answer. It bridges the gap between an LLM's static training data and your dynamic, domain-specific content.
How It Works
A RAG pipeline has two stages:
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Retrieval: When a user submits a query, the system converts it into an embedding and searches a vector database (or other index) for the most semantically relevant documents. These might be internal documentation, code repositories, product manuals, or any curated content.
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Generation: The retrieved documents are injected into the LLM's prompt as additional context. The model then generates a response grounded in this specific information, rather than relying solely on what it learned during pre-training.
This two-stage approach means the LLM always has access to current, authoritative information without requiring expensive retraining or fine-tuning.
Why It Matters
LLMs are trained on data with a knowledge cutoff and can hallucinate facts they were never trained on. RAG mitigates both problems by anchoring responses in real, retrievable documents. It also lets you control exactly what information the model can reference, which is essential for enterprise use cases involving proprietary or sensitive data.
In Practice
When deploying an AI coding assistant, RAG enables the agent to reference your project's actual codebase, documentation, and configuration files. Instead of guessing at your API structure or deployment setup, the assistant retrieves the relevant source files and provides answers grounded in your real code. This dramatically improves accuracy and makes the assistant genuinely useful for project-specific questions, troubleshooting, and development tasks.