Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is an advanced AI framework that combines traditional information retrieval systems with generative large language models (LLMs), enabling AI to generate text that is more accurate, current, and contextually relevant by integrating external knowledge.

Definition

Retrieval-Augmented Generation (RAG) is an AI architecture in which a generative large language model (LLM) is paired with an external retrieval system. At query time, the retriever fetches the most relevant chunks of text from a knowledge source — a vector index, database, or document store — and the LLM uses those chunks as additional context when producing its answer.

The LLM is no longer limited to what it learned during training: its answers reflect whatever is in the connected knowledge base, which can be updated continuously without retraining the model.

How RAG works

A RAG pipeline has two stages:

  1. Retrieval — the user query (or a transformed version of it) is embedded and matched against an index of pre-embedded knowledge chunks. Top-k matches are returned, often re-ranked for relevance.
  2. Generation — the retrieved chunks are inserted into the LLM’s prompt as context. The LLM produces an answer grounded in those chunks, typically with citations.
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Components

  • Retrieval system — vector database (e.g. Pinecone, Weaviate, Qdrant), keyword index, or hybrid retriever; embedding model; optional reranker.
  • Generative model — any LLM (GPT, Claude, Gemini, Llama, Mistral) that accepts the retrieved chunks as context.
  • Knowledge source — documents, websites, databases, or any indexed corpus the retriever pulls from.

When to use RAG

  • Customer support — chatbots that need current product, policy, or pricing information.
  • Internal Q&A and knowledge management — employee-facing assistants over the company’s documents and wiki.
  • Research and analysis — querying large document sets where the LLM needs up-to-date evidence.
  • Regulated domains — legal, healthcare, finance, where every claim must cite a verifiable source.

RAG is the right choice whenever the answer depends on data that changes over time or that wasn’t in the LLM’s training set.

For an in-depth comparison with Cache-Augmented Generation (CAG) — the alternative that preloads a static knowledge set into the model’s context instead of retrieving at query time — see our blog post RAG vs CAG: which augmentation strategy fits your project .

For RAG with autonomous tool use and multi-step reasoning, see Agentic RAG .

Build RAG flows with FlowHunt

With FlowHunt you can index knowledge from any source on the internet — your website, PDFs, Google Search, Reddit, Wikipedia — and use it to power content generation or customer-support chatbots without writing retrieval code.

RAG with Google Search

Further reading

Frequently asked questions

Try RAG-Based AI Flows with FlowHunt

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Retrieval vs Cache Augmented Generation (CAG vs. RAG)
Retrieval vs Cache Augmented Generation (CAG vs. RAG)

Retrieval vs Cache Augmented Generation (CAG vs. RAG)

Discover the key differences between Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) in AI. Learn how RAG dynamically retrieves real-t...

5 min read
RAG CAG +5