Retrieval

Retrieval-augmented generation

RAG combines search with generation to answer questions using external data.

Quick definition

RAG combines search with generation to answer questions using external data.

  • Category: Retrieval
  • Focus: grounded answers and search relevance
  • Used in: Question answering over internal docs or knowledge bases.

What it means

It retrieves relevant documents and uses them as context for the model. In retrieval workflows, retrieval-augmented generation often shapes grounded answers and search relevance.

How it works

Retrieval pipelines index content into chunks and embeddings, then fetch relevant pieces at query time. The model uses those snippets as context to answer.

Why it matters

Retrieval improves accuracy by grounding responses in your data.

Common use cases

  • Question answering over internal docs or knowledge bases.
  • Support assistants that cite sources and reduce hallucinations.
  • Enterprise search that understands intent beyond keywords.

Example

Search docs for a feature, then generate a response.

Pitfalls and tips

Poor chunking or stale data leads to irrelevant results. Refresh indexes and tune chunk size to keep answers accurate.

In BoltAI

In BoltAI, this appears in search, knowledge, and grounding workflows.