Frame the grounded café FAQ application experiment

A lab needs a falsifiable claim before code. The claim here is that combine input validation, five-note retrieval, a cited answer, tests, logs, and a two-minute demo into one finishable application. Record the tiny dataset, expected behavior, and one reason the result could be misleading. The first artifact is an experiment brief, not a model screenshot. It names the user, the decision the output supports, and the baseline you must beat. For this chapter, the baseline is deliberately transparent so later complexity has something honest to compare against.

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. Run the inventory below before implementing anything. Its output proves that the fixture is present and small enough to inspect by hand.

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

docs={'hours':'Cafe open 8am to 6pm weekdays.','wifi':'Ask staff for the Wi-Fi card.','pets':'Service animals are welcome.'}
print(sorted(docs),len(docs))

Expected output: three sorted IDs and count 3. 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 experiment brief 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 experiment brief 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.