Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation solves one of the biggest limitations of LLMs: they only know what was in their training data. RAG fixes this by adding a retrieval step — before the model generates an answer, the system searches a knowledge base for relevant information and includes it in the prompt.

The typical RAG pipeline works in three steps. First, documents are split into chunks and converted to embeddings (numerical representations). Second, when a question comes in, the system finds the most semantically similar chunks using vector search. Third, those chunks are injected into the LLM's context alongside the question, grounding the response in actual data.

RAG is critical for business applications where accuracy matters. An AI customer support agent needs access to your documentation. An AI developer agent needs access to your codebase. An AI marketing agent needs access to your brand guidelines. Without RAG, these agents would hallucinate. With it, they give precise, source-backed answers. At Agentik {OS}, every agent that needs domain knowledge uses RAG to stay grounded in your specific data.