Last reviewed: 2026-05-04
Retrieval-augmented generation (RAG) is an AI technique that grounds large language model responses in trusted source documents retrieved at query time. Instead of relying only on the model’s trained knowledge, RAG fetches relevant content from a knowledge base and gives it to the LLM as context — reducing hallucinations and improving factual accuracy.
Why retrieval-augmented generation (RAG) matters
- Reduces hallucinations. Grounding responses in retrieved source material is the single most effective defence against LLM fabrication.
- Uses your data. RAG lets an LLM answer questions about your products, policies, and knowledge without retraining.
- Stays current. Update the knowledge base and the answers update — no model retraining required.
- Auditable answers. Good RAG systems cite the source document, which matters in regulated industries.
- Cheaper than fine-tuning. For most enterprise use cases, RAG is faster and cheaper than training a custom model.
- Works with any LLM. RAG is model-agnostic, which preserves optionality as the model landscape shifts.
How retrieval-augmented generation (RAG) works
A RAG pipeline has four stages:
- Indexing. Documents are chunked, embedded as vectors, and stored in a vector database.
- Retrieval. At query time, the user’s question is embedded and matched against the vector store to find relevant chunks.
- Augmentation. Retrieved chunks are assembled into the LLM prompt as context.
- Generation. The LLM generates a response grounded in the retrieved content, ideally citing sources.
How to measure
- Answer accuracy — percentage of answers factually correct against source.
- Hallucination rate — frequency of claims not supported by retrieved content.
- Retrieval precision — percentage of retrieved chunks actually relevant to the query.
- Retrieval recall — percentage of relevant chunks that were retrieved.
- Citation accuracy — percentage of citations that correctly point to the source.
- End-to-end resolved interaction rate — the business metric, not just the technical one.
How to improve performance
- Invest in chunking strategy. Poor chunking is the #1 cause of weak retrieval quality.
- Evaluate retrieval separately from generation. If the wrong chunks are retrieved, no model can recover.
- Use hybrid search. Combine vector similarity with keyword search for best recall.
- Cite sources in the response. Auditability is a product feature, not a technical detail.
- Enforce output control on compliance turns. Even with RAG, regulated content should use deterministic responses.
- Monitor for drift. As your knowledge base grows, retrieval quality can silently degrade.
The Teneo perspective on retrieval-augmented generation (RAG)
Teneo uses retrieval-augmented generation as one component of a broader strategy for reducing LLM risk in enterprise contact centers. Four principles: 100% output control via TLML for compliance-sensitive turns where even grounded generation is too risky; LLM-independence by design so the same RAG architecture runs across GPT, Claude, Gemini, or a private model; the best integrations engine in the category for connecting RAG to the real knowledge bases, CRMs, and product catalogs enterprises maintain; and a focus on resolved interactions, not deflected calls — a grounded answer that does not resolve the issue is still a failure.
Explore the Teneo Agentic AI platform or read our guide on conversational AI for the enterprise.
FAQ
What is retrieval-augmented generation in simple terms?
Retrieval-augmented generation is a way to make an AI answer using your documents instead of only what it learned during training. It searches your knowledge base for relevant content, gives that content to the LLM as context, and asks it to answer. The result is more accurate, more current, and easier to audit.
How does RAG reduce hallucinations?
By grounding the LLM’s response in retrieved source material. When the model has the actual answer in its context, it is much less likely to fabricate. RAG does not eliminate hallucinations entirely — especially when retrieval is poor or the model misreads the context — but it reduces them significantly and makes them easier to catch.
What is the difference between RAG and fine-tuning?
RAG retrieves information at query time; fine-tuning bakes information into the model weights. For enterprise knowledge that changes — product specs, policies, pricing, FAQs — RAG is usually better because updates are instant. Fine-tuning is useful for teaching the model style, format, or domain-specific reasoning patterns that do not change often.
What is a vector database and do I need one for RAG?
A vector database stores document chunks as numerical embeddings and retrieves them by semantic similarity. Most RAG systems use one, though hybrid approaches combine vector search with keyword search for better results. Vector databases are the standard foundation — but they are not the whole story; chunking and retrieval strategy matter more.
Can I use RAG for regulated industries like banking or healthcare?
Yes, with care. RAG is safer than unconstrained generation because answers are grounded and auditable, but even grounded responses can misread context. The best practice in regulated industries is hybrid: RAG for informational turns, deterministic responses for compliance-sensitive turns, with clear output control over both.
What is the biggest mistake people make with RAG?
Underinvesting in retrieval quality. Teams pick a model, a vector database, and a chunking strategy in an afternoon and wonder why quality is poor. Retrieval precision and recall should be evaluated and tuned before the generation quality is even measured. Bad retrieval defeats the best LLM.
Related terms
- Large Language Model (LLM)
- Vector Database
- LLM Hallucinations
- LLM Orchestration
- AI Prompt
- Natural Language Understanding (NLU)
- Agentic AI
- Contact Center AI