AI & Voice

RAG (Retrieval-Augmented Generation)

Technique where an LLM is given relevant documents/data at query time, instead of relying solely on its training data.

RAG works in two steps: retrieve relevant context (from a vector database, search index, or knowledge base) based on the user's query, then pass that context plus the query to an LLM to generate a grounded response.

Why it works: LLMs hallucinate when they don't know answers. RAG grounds them in actual data — your product docs, policy manuals, customer history. The LLM stops making things up because it has the real source material.

Indian B2B chatbots use RAG extensively: support bots answer from FAQ + ticket history, sales bots from product specs, internal bots from HR policies. Performance depends on retrieval quality (vector embeddings + filtering) more than LLM choice.

India context

Indian SMBs running customer-support bots should use RAG over their FAQ + past ticket data. Without it, the LLM hallucinates wildly — 'no, you don't qualify for that' when the actual policy says yes.

Examples

  • A salon's WhatsApp bot uses RAG over the salon's services + pricing + policies — answers questions accurately.
  • A SaaS company's support bot retrieves relevant docs + past tickets before answering.

FAQ

What's the difference between RAG and fine-tuning?

RAG retrieves documents at query time; fine-tuning bakes knowledge into model weights. RAG is faster to update, fine-tuning is more efficient at scale. Most production systems use RAG.

How is retrieval done in RAG?

Documents are converted to vector embeddings and stored in a vector database (Pinecone, Weaviate, pgvector). User query is embedded the same way; nearest neighbors are retrieved.

Does RAG eliminate hallucinations?

Mostly. The LLM is grounded in retrieved context — but if the retrieval misses the right document, the LLM may still hallucinate. Quality of embeddings + chunking strategy matters.

Related concepts

LLMvector databaseembeddinghallucinationcontext window

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Related glossary entries

LLM (Large Language Model) — AI model trained on vast text data — generates human-like text, answers questions, follows instructions.Hallucination (AI) — When an LLM generates plausible-sounding but factually incorrect or fabricated content.

More in AI & Voice

STT (Speech-to-Text)TTS (Text-to-Speech)LLM (Large Language Model)Hallucination (AI)
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