AI
Hallucination Review Workspace
Compare an answer against source text and flag likely unsupported claims before you trust or publish the result.
Source textGrounding material
Paste the reference content the answer is supposed to rely on.
AnswerCandidate model output
Paste the AI answer you want to check for unsupported claims.
SupportedLikely grounded
The incident raised latency for the API from 13:02 to 13:19 UTC.
UnsupportedLikely hallucinated
No data loss occurred, and mitigation was a traffic rollback.
The incident also caused customer data loss.
Source-aware review
This workspace is useful for manual answer checking, RAG spot checks, and editorial review before you invest in formal hallucination evals.
Use this tool when
These are the practical situations where this workflow usually earns its keep.
You are still shaping the prompt, schema, trace, eval case, or safety posture and want a fast local iteration loop first.
You need a review-friendly artifact before sending work into a live model, batch eval, or agent integration.
You want to compare or inspect AI workflow material without exposing internal prompts or source text more widely than necessary.
Prompt and output iteration
Local AI tools shorten the cycle between seeing a weakness and tightening the prompt, schema, or answer shape that caused it.
Eval and safety preparation
Teams can build rubrics, adversarial cases, or review datasets before they invest in heavier automation or model-backed test runs.
Trace and workflow debugging
A smaller local surface helps reviewers understand tool-call churn, unsupported claims, context drift, or grounding gaps before they open a larger incident or quality review.
Common mistakes to avoid
These are the checks that usually keep the output useful instead of misleading.
Treating heuristic local checks as definitive proof of model quality or safety.
Testing only polished examples instead of the messy or adversarial inputs users will actually create.
Moving prompts or traces into external systems before checking policy and data handling expectations.
Learn how to use this tool
Compare answers against source text and flag likely unsupported claims. This guide is aimed at AI workflow design work where teams need clearer prompts, safer reviews, or better eval preparation before spending tokens or shipping behavior.
Read the guideTell us what is missing
If this flow helped only partly, leave feedback so we can understand the missing step or edge case.
Leave feedbackRequest the next tool
Use the wishlist to suggest the next utility, workflow, or improvement that would complete this job to be done.
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Score AI outputs for format, groundedness, safety, and completeness with a local rubric.
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