The outcome

Answer from an approved corpus with measurable retrieval quality instead of relying on unsupported model memory.

Step by step

A workflow you can repeat.

  1. 01

    Define the questions, corpus owners, access rules, freshness, languages, citation format, and a benchmark with relevant and unanswerable cases.

  2. 02

    Clean and chunk approved documents, preserve source IDs and permissions, then embed documents with the correct input type and pinned model.

  3. 03

    Embed each query with the search-query input type, retrieve a broad candidate set, and rerank candidates using stable document identifiers.

  4. 04

    Send only top verified passages to Chat with an instruction to cite sources and abstain when evidence is missing or contradictory.

  5. 05

    Measure retrieval recall, reranking quality, answer support, latency, and billed units; version the index and rerun tests on model changes.

Working standard

What good use looks like.

  • Use the correct embedding input types.
  • Evaluate retrieval before generation.
  • Preserve source IDs through reranking.

Official references

Check the current product documentation.