Untrusted retrieval is now a more common production failure than a weak prompt, because agents ingest memory they never score. A seventy-minute workshop can add a cheap scoring gate, a replayable log, and a reject path before generation. Students leave with a runnable Python harness, a four-row decision table, and a timing plan they can repeat. The method stays useful if every product name is removed and the scoring host is only a free server.
What you will build
This workshop treats retrieved snippets as untrusted input, not as ground truth the model should quote. You will capture a retrieval batch, score each chunk against a written rubric, and allow only passing chunks into the prompt. A JSONL replay log records the fingerprint, score, and decision so later failures can be diffed. The generation model never sees dropped text, which keeps stale or planted memory out of the answer.
Timing box
00:00–00:10 — install dependencies, copy the harness, and load the sample corpus
