---
title: "What is RAG (Retrieval-Augmented Generation)?"
canonical: https://wavect.io/glossary/rag/
language: en
description: "Inject relevant context into an LLM prompt at runtime, retrieved from your own data, so the model answers from your knowledge instead of its training data."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

TECHNOLOGIES

# RAG

Retrieval-Augmented Generation

Inject relevant context into an LLM prompt at runtime, retrieved from your own data, so the model answers from your knowledge instead of its training data.

Last reviewed: 2026-05-24 by [Kevin Riedl](/team/kevin-riedl/) [wiki ↗](https://www.wikidata.org/wiki/Q139796365)

RAG is the architecture that lets an LLM answer questions about data it was not trained on. The mechanism is straightforward: take the user’s question, retrieve the most relevant chunks from your corpus (via vector search, keyword search, or a hybrid), stuff them into the prompt, and let the model answer. It is the grounding layer underneath most useful [AI agents](/glossary/ai-agents/).

The reason RAG exists: training a model on your private data is expensive, slow, and obsolete the moment your data changes. RAG sidesteps that by treating the data as runtime context. The trade-off is that retrieval quality becomes the bottleneck: a model with bad context produces confidently wrong answers.

Worked example of the classic failure: a company builds a support bot over its help docs, the demo is impressive, and then in production it confidently cites the wrong refund policy. The instinct is to blame the model and try a bigger one. The answers stay wrong, because the problem is upstream: the docs were chunked mid-sentence, the embeddings cannot tell “refund” from “return”, and there is no reranker to push the right passage to the top. Swap in better chunking, hybrid search, and a reranker and the same model suddenly looks smart. The lesson generalises: when RAG is wrong, suspect retrieval before generation almost every time.

The honest trade-off and where RAG breaks down: it excels at lookup-shaped questions answerable from a few passages, and degrades when the answer requires synthesising across many documents or reconciling contradictions in the corpus. At that point you want a smaller curated corpus, an agentic pipeline that reasons in steps, or fine-tuning, not a bigger retriever. The unsexy truth is that 80% of the work is making retrieval good (chunking, embeddings, reranking, hybrid search) and 20% is the model. Wrapping your data sources as [MCP](/glossary/mcp/) servers makes the retrieval layer portable, but it does not make it good. Vendors that pitch RAG as a one-click feature are pitching the easy part.

// FAQ

## FAQs

### Why does my RAG system give wrong answers?

Almost always retrieval, not generation. Bad chunking, weak embeddings, no reranking, or a corpus the model cannot disambiguate. Swap the model and the answers stay wrong; fix retrieval and the same model suddenly looks smart.

### Vector search or hybrid search?

Hybrid (vector + keyword + reranker) beats vector-only in almost every production benchmark we have run. Pure vector search misses on exact matches, acronyms, and rare terms. The extra plumbing is worth it.

### When does RAG break down?

When the answer requires synthesising across many documents, when the corpus has heavy contradictions, or when the user query is more conceptual than lookup-shaped. At that point you want either fine-tuning, an agentic pipeline, or a smaller better-curated corpus.

Related terms

- [AI Agents](/glossary/ai-agents/)
- [MCP](/glossary/mcp/)

Related services

- [Artificial Intelligence](/services/artificial-intelligence/)

Related case studies

- [Quivr](/case-studies/quivr/)
- [PromptID](/case-studies/promptid/)

Related guides

- [How to choose a tech stack for an MVP](/software-development-guide/how-to-choose-a-tech-stack-for-mvp/)

Discussed in articles

- [RAG vs fine-tuning vs long context (2026)](/blog/rag-vs-finetune-vs-longcontext-2026/)
- [RAG production-readiness checklist for the EU](/blog/rag-production-readiness-checklist-eu/)

Talk to us

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