Hallucination & Grounding — Learning Bundle
This learning bundle compares a failure with a way to reduce it. Hallucination is model output with no support. Grounding ties claims to evidence that's given, found, or checked. The bundle is not one standard quality metric.
Another example
A research assistant doesn't make up a launch date. It links each factual claim to a source.
Main parts
- 01User intent and context
- 02Model or tool decision
- 03Grounded result and fallback
Use it when
Study the failure and its fix separately. For important claims, require clear evidence, refusal when unsure, citations, and checks.
Do not use it when
Never use confident-sounding words in place of evidence.
Name used in code
citations + abstention Before you ship
Check that every member concept has its own name and link. Show all members in the same situation. Give a visible rule for when each one applies and which groups are valid. Never present the bundle as one canonical pattern or as parts that can be swapped freely.
Outcome: Use Hallucination & Grounding — Learning Bundle to compare its member concepts. Keep their boundaries clear, then decide which members apply and which can work together. User context: A research assistant doesn't make up a launch date. It links each factual claim to a source. Learning bundle to unpack: Hallucination & Grounding — Learning Bundle. Canonical members: AI Hallucination, AI Grounding Why this comparison helps: Study the failure and its fix separately. For important claims, require clear evidence, refusal when unsure, citations, and checks. Do not treat the bundle as one pattern when: Never use confident-sounding words in place of evidence. Comparison requirements: Name every member concept on its own. Explain its job and limits. Show every member in the same real situation. Give a visible sign for when to use, combine, or leave out each member. Decision quality: Keep platform and evidence limits clear. Show which members can work together. Point out false matches, unsafe swaps, and cases that need another concept. Acceptance criteria: A reader can explain each member in one sentence and tell them apart in the example. They can defend the chosen member or valid group and open every canonical guide. Evidence and limits (evidence boundary): This is not a normative standard. Its name, limits, and expected behavior can change from one source to another. Unknowns to confirm: Target platform, decision setting, limits, and evidence needed to use, combine, or leave out members.
Check this request
Review the current use of Hallucination & Grounding — Learning Bundle. Definition: This learning bundle compares a failure with a way to reduce it. Hallucination is model output with no support. Grounding ties claims to evidence that's given, found, or checked. The bundle is not one standard quality metric. Release checks: Check that every member concept has its own name and link. Show all members in the same situation. Give a visible rule for when each one applies and which groups are valid. Never present the bundle as one canonical pattern or as parts that can be swapped freely. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.