Page 5 of 8~120 min topic

Build a mini RAG

Debug retrieval miss or ungrounded fluent answer in the café FAQ retriever

Page 5 reproduces and repairs the characteristic failure of the five-document café FAQ retriever: retrieval miss or ungrounded fluent answer.

~15 min this pageDebugging

1Learn the idea

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Reproduce before you repair

Do not start with a speculative fix for the café FAQ retriever. Force the failure on purpose, save the before output, then change one cause at a time. Retries are allowed only for transient conditions—not for bad input that will fail forever on build-mini-rag.

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Force the failure

def answer(question,docs):
 words=set(question.lower().split()); scored=[(len(words&set(text.lower().split())),k,text) for k,text in docs.items()]
 score,k,text=max(scored)
 return {'answer':text,'source':k} if score else {'answer':'Not in the FAQ.','source':None}
print(answer('refund policy?',{'hours':'open weekdays'}))

Expected evidence: Not in the FAQ with source None. If you cannot reproduce on demand, you do not yet control the failure mode for build-mini-rag.

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Repair with a reviewable diff

After repair, rerun the exact reproduction command. Keep the failing fixture as a regression seed for the observability page. For the five-document café FAQ retriever, remember the claim you are restoring: overlap retrieval returns the right note or abstains on zero overlap.

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Lab notebook: reproduce on command

Store a one-command reproduction for: retrieval miss or ungrounded fluent answer. The command should use café notes: hours, wifi, pets, allergens, parking or a minimal mutant of it. Paste the failing output into notes/failure-before.txt (or your shell scrollback as copied text). After the fix, paste notes/failure-after.txt and keep both.

Retries belong only on transient faults. If the failure is bad input, a bad allowlist, or a logic bug in the café FAQ retriever, retrying will amplify cost without repairing trust around answer a café question only when a relevant local note is retrieved and cite its ID.

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Worked judgment

Classify the failure as prevent, detect, contain, or recover—using this lab’s language, not a generic poster. For build-mini-rag, the first fix should usually be detect+prevent at the boundary, because retrieval miss or ungrounded fluent answer is cheaper to stop early than to explain in production prose.

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Why this stage matters for the café FAQ retriever

At the debugging stage for build-mini-rag, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about café notes: hours, wifi, pets, allergens, parking that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: always-answer-from-largest-document heuristic.

For this page specifically, success looks like before/after evidence for the characteristic failure while still centering the user decision to answer a café question only when a relevant local note is retrieved and cite its ID. If you cannot point to a file, command, or assertion that proves that for the café FAQ retriever, stay on this page instead of advancing.

How-to: build a 5-document RAG app · Glossary: RAG

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Before you start

Why this matters

Describe the smallest fixture that triggers retrieval miss or ungrounded fluent answer. Predict the first visible symptom (exception, wrong label, silent empty success). You will compare that prediction with the reproduction below.

In the wild

See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

1. Can you reproduce the failure with a one-command fixture?
2. Did you avoid retrying non-transient bad input?
3. Is before/after evidence saved as text (not only a screenshot)?
4. Does the repair restore the metric path toward: retrieval hit rate, citation correctness, supported-answer rate, abstention accuracy?

All responses are required.