01 / DECIDE
Architecture decision
Use it when answers must be grounded in a controlled corpus rather than relying on model memory.
Embeddings support semantic comparison; a production retrieval layer also needs chunk ownership, metadata filters, freshness, citations, and deletion. Map input, output, state, and side effects as one observable path before deciding which layer owns separate retrieval from generation.
02 / BUILD
Three-step implementation
- 01
Frame the contract
Write down the caller, data classification, success condition, timeout, cancellation, and ownership. Use it when answers must be grounded in a controlled corpus rather than relying on model memory.
- 02
Build one narrow path
Implement one end-to-end path with request correlation, typed state, and reversible failure handling. Embeddings support semantic comparison; a production retrieval layer also needs chunk ownership, metadata filters, freshness, citations, and deletion.
- 03
Prove the outcome
Turn acceptance into a repeatable fixture, contract test, or browser test. A golden query set measures relevant recall, citation precision, stale-document leakage, and no-answer behavior.
03 / BOUND
Production boundary
Enforce document-level authorization before retrieval and remove deleted or revoked content from every derived index.
04 / PROVE
Acceptance evidence
A golden query set measures relevant recall, citation precision, stale-document leakage, and no-answer behavior.
SOURCE / HTTP
Reproducible source probe
curl -fsSI 'https://developers.openai.com/api/docs/guides/embeddings' | sed -n '1,5p'