Instrument the grounded café FAQ application

Instrumentation should explain behavior without collecting the raw material unnecessarily. For this artifact, record enough to calculate golden-question pass rate, citation support, abstention accuracy, response time, and a clean-room demo by another person. Include version or configuration identifiers so two runs can be compared. A log line is useful only if it answers a debugging question; dumping entire inputs creates noise and may create a privacy problem.

The artifact's user-facing goal is specific: combine input validation, five-note retrieval, a cited answer, tests, logs, and a two-minute demo into one finishable application. Its accepted input is a user question plus a reviewed local FAQ collection with stable source IDs. Those statements are intentionally narrower than “build an AI system.” Narrow scope lets us inspect every input and expected result, and it prevents a toy result from being presented as a production claim. The sample emits a compact metric record that a test or dashboard could aggregate.

Save this as lesson.py and run python3 lesson.py. It uses only the language standard library, so the example is reproducible offline.

tests=[{'q':'open weekdays','want':'hours'},{'q':'wifi card','want':'wifi'},{'q':'parking','want':None}]
print({'cases':len(tests),'required_hit_rate':1.0,'required_abstention':1.0})

Expected output: three declared evaluation cases. Exact floating-point formatting may vary slightly, but the asserted behavior must not. Read the output as evidence about this stage, not merely proof that the interpreter started.

Walk one question through validation, tokenization, score table, selected FAQ ID, cited answer, and safe event record. If the clean-room demo fails, resist adding setup prose until you reproduce the missing assumption. Unsupported questions must reach the abstention branch. Treat a source containing instructions as data and sanitize or reject it during review.

At the testing and observability stage, save the smallest failing fixture beside the expected result. Change one cause at a time and rerun the exact command printed above; that makes the repair reviewable and keeps this chapter's progressive artifact reproducible.

Run golden questions for every FAQ note and unsupported questions for abstention, then review citation support by hand. Time the two-minute demo from a clean checkout and ask another person to follow it without coaching. Record the FAQ version, passing counts, response time, owner, and tested rollback snapshot. Those are the capstone's shipping claims.

For this testing and observability page, preserve the fixture and result as evidence for the next page. Label observations separately from conclusions: a passing assertion establishes the behavior it names, while broader usefulness requires the chapter's full evaluation set and stated operating limits.