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Retrieval-augmented generation

Ground answers in your data.

Retrieval

Overview

Retrieval-Augmented Generation grounds an LLM in external knowledge by retrieving relevant chunks and injecting them into the prompt. It reduces hallucination and lets models answer over private, up-to-date data.

How it works

Retrieval
IndexRetrieveAugmentOutputEmbed docsembeddingsTop-ksimilarityInjectgroundingGrounded
ClientServiceEdgeData

Step by step, with examples

  1. 1

    Embed docs

    • Chunk documents into a vector store.
  2. 2

    Top-k

    • ANN search for the query.
  3. 3

    Inject

    • Add retrieved snippets to the prompt.
  4. 4

    Grounded

    • Answer with citations.
    • Example: cuts hallucination

Overview

Chunk + embed a corpus, retrieve top-k by similarity, and inject the snippets so the model answers from grounded facts.

Common pitfalls

  • Bad chunking
  • No reranking
  • Stale index
  • No citation/grounding check

Where this content comes from

For full transparency, this content is curated and verified from these sources:

Frontier-lab prompting & agent guidesRetrieval-augmented generation literatureOppZen-authored context-engineering playbooks