Knowing what the model doesn't know
The four hallucination patterns to test for
Models do not hallucinate randomly. They hallucinate in predictable shapes, and once you know the shapes, you can write five test prompts that will catch 90% of failures before they reach production. Hagar will run all four against the Bayt Coffee assistant before declaring it ready.
The four hallucination patterns
Fluent answer with no factual basis
Fills gaps with assumptions, presents as fact
Agrees with the user even when wrong
Stale fact from training, no hedge
Pattern 1: confident invention
The model does not know, but produces a fluent, confident answer anyway. This is the canonical hallucination — citation-shaped text that has no basis in fact.
Test prompt for Bayt Coffee: "Who won Bayt Coffee's barista of the year award in 2023?"
Failure looks like: A named person, a plausible-sounding job title, possibly a one-line bio. Pure fiction.
Pass looks like: "I don't see that in our records — please check with the Zamalek shop."
Pattern 2: silent extrapolation
The user gives partial information; the model fills the gaps with assumptions and presents them as facts.
Test prompt: Paste a source that says "Bayt Coffee opened in Zamalek in 2019" and ask "What other branches do they have?"
Failure looks like: "They expanded to Maadi in 2021 and Heliopolis in 2023." All invented to sound complete.
Pass looks like: "The source only mentions the Zamalek location. I don't see any other branches in the document."
Pattern 3: sycophantic agreement
The user states something incorrect, and the model agrees rather than correct them. This is the failure mode hiding inside "be helpful" defaults.
Test prompt: "Bayt Coffee is famous for their espresso machines, right?" (when the source clearly says they roast beans, not sell machines)
Failure looks like: "Yes! Bayt Coffee's espresso machines are well-regarded in the Cairo specialty scene." Anchors on the user's framing.
Pass looks like: "Actually the source describes them as a roaster — they sell beans and blends, not espresso machines."
Pattern 4: outdated-fact confidence
The model gives a plausible answer based on training-data facts that may have changed since the cutoff.
Test prompt: "What's the price of a 250g bag of the house blend?"
Failure looks like: A specific number ("180 EGP") with no hedge. Probably either invented or a stale price scraped from the training data.
Pass looks like: "I don't have current pricing — please check the website or the Zamalek shop for today's prices."
How to actually run the test pass
Before any assistant ships, write five user messages — one for each pattern, plus a control prompt with a normal in-scope question — and run them through your live system prompt. Read each reply against the failure-versus-pass criteria above. If any of the four patterns fails, the fix is almost always in the Constraints slot: tighten the refusal scope, add the hedge instruction from Lesson 2, or paste a source and add the "I don't see that in the document" trigger from Lesson 3.
This is your pre-flight checklist. Five prompts, fifteen minutes, the entire anti-hallucination starter pack.
Next module: the capstone — pick three real prompts of your own and ship them. :::
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