What is RAG (Retrieval-Augmented Generation)? Definition & Guide

What is RAG (Retrieval-Augmented Generation)?

etrieval Augmented Generation (RAG) is a technique that enhances large language model outputs by first retrieving relevant documents from a knowledge base, then providing them as context to the model when generating a response. RAG grounds AI answers in verified enterprise content, dramatically reducing hallucination. Enterprise RAG pipelines index internal documents, policies, and SOPs using embeddings and serve them from a vector database.

A RAG pipeline has two distinct phases. Offline indexing: enterprise documents are split into chunks, converted to embedding vectors, and stored in a vector database. This runs once and is updated incrementally as documents change. Online retrieval and generation: when a query arrives, it is converted to an embedding vector, the vector database retrieves the most similar document chunks, these chunks are added to the LLM's prompt as context, and the LLM generates a response grounded in that context. The quality of the final response depends on three independent factors: the quality of the indexing (did the right documents get indexed?), the quality of the retrieval (did the right chunks get retrieved?), and the quality of the generation (did the model reason correctly from the retrieved context?).

Advanced RAG techniques address the failure modes of naive RAG implementations. Naive RAG retrieves the top-N most similar chunks by vector similarity and provides them all to the model. This fails when: the most similar chunks are not the most informative (similarity ≠ relevance), when the query needs information from multiple unrelated documents that won't appear in a single similarity search, or when the chunks lack enough surrounding context to be useful standalone. Advanced RAG techniques include query rewriting (reformulating the query to retrieve better chunks), hybrid search (combining vector similarity with keyword matching), re-ranking (using a second model to reorder retrieved chunks by actual relevance), and recursive retrieval (following references across documents).

Also known as: Retrieval Augmented Generation, Grounded Generation

Key Points

Core idea
RAG dramatically reduces hallucination by constraining the model to summarise retrieved documents rather than generate from memory. The model cites sources; users and auditors can verify claims.
Why it matters
Offline: index documents into a vector database. Online: retrieve relevant chunks for the query. Then: generate a response grounded in those chunks. Quality at each stage independently affects final answer quality.
Enterprise use
Hybrid search (vector + keyword), re-ranking, and query rewriting address the failure modes of naive top-N vector retrieval — essential for production enterprise RAG at scale.

How It Works

How RAG (Retrieval-Augmented Generation) works

Define the purpose, inputs, and success criteria that RAG (Retrieval-Augmented Generation) must support.
Apply RAG (Retrieval-Augmented Generation) in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

On-premise RAG grounded in your enterprise documents.

Fluid AI's RAG pipeline indexes enterprise documents, SOPs, and policy files in a private vector database. Every agent response is grounded in verified source content, with citations.

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Topics Covered

RAG enterprise AI
retrieval augmented generation banking
on-premise RAG deployment
RAG hallucination reduction
enterprise RAG pipeline
vector database RAG enterprise
advanced RAG techniques
RAG knowledge base enterprise

RAG (Retrieval-Augmented Generation)