AI Hallucination & Factual Accuracy Playbook
Evaluates AI-generated conversations for factual correctness and unsupported claims. Cross-references outputs against approved knowledge sources to detect hallucinations and accuracy issues.
Start building
Deploy this kit stack into your workspace. Customize bricks, scoring, and outputs to match your team.
Without this playbook
Most teams handle ai hallucination & factual accuracy through scattered call reviews, manager opinion, and isolated examples. Without a shared operational definition, the signals stay inconsistent and difficult to act on across volume.
With this playbook
A shared, repeatable lens for ai hallucination & factual accuracy - with structured outputs you can route into coaching, reporting, and workflow automation. Every conversation produces evidence, not just opinions.
Built for
AI product managers, ML engineers, and trust & safety teams
When teams use it
- Model evaluation and release gates
- Governance review and policy enforcement
- Safety and accuracy monitoring
The operational stack
3 kits behind this playbook
AI hallucination is not a single failure mode - it ranges from confidently wrong facts to subtly unsupported claims. This stack separates the problem into three layers: verification against approved knowledge sources to catch outright factual errors, unsupported claim detection to flag assertions that lack grounding even if they sound plausible, and a confidence score that measures how well-supported the overall output is. This lets teams distinguish between a hallucinated fact and a poorly grounded inference, which require different fixes.
Accuracy Verification Kit
3 bricks
Cross-references facts against approved sources to detect errors in AI or semantic output.
Included bricks
Review this kitVerifiable Claims
String listExtracts claims that are specific enough to be fact-checked against an external or KB source.
Factual Accuracy Score
ScoreScores how factually accurate the claims made in the conversation are, based on the knowledge base.
Unsupported Claim Present
BooleanDetects whether any claim was made without sufficient supporting evidence from the knowledge base.
Factual Confidence Kit
2 bricks
Scores confidence and supportedness of factual signals.
Included bricks
Review this kitFactual Support Score
ScoreScores how well claims are supported by verifiable evidence from the knowledge base - distinct from factual correctness.
Unsupported Claim Present
BooleanDetects whether any claim was made without sufficient supporting evidence from the knowledge base.
Unsupported Claim Detection Kit
3 bricks
Flags claims without evidence support.
Included bricks
Review this kitVerifiable Claims
String listExtracts claims that are specific enough to be fact-checked against an external or KB source.
Claim Supported By Kb
BooleanDetects whether every specific verifiable claim in the conversation is directly supported by the knowledge base. Returns false if any claim lacks KB support.
Unsupported Claim Present
BooleanDetects whether any claim was made without sufficient supporting evidence from the knowledge base.
Knowledge base
Supporting materials
The kits in this playbook work best when backed by reference materials that ground the evaluation. Upload these into your workspace knowledge base to improve accuracy and relevance.
Learn more about Knowledge BasesApproved knowledge base and source-of-truth documentation
Product documentation, FAQs, and help centre content
Factual reference materials the AI should ground responses in
Known hallucination patterns and failure mode documentation
Evaluation rubrics for factual accuracy and confidence thresholds
Structured output
What you get back
Every conversation processed through this stack produces a structured JSON object. Each brick contributes a typed field - booleans, scores, categories, or string lists - that you can route, aggregate, and report on.
Example output shape
{
"verifiable_claims": [
"signal 1",
"signal 2"
],
"factual_accuracy_score": 7,
"unsupported_claim_present": true,
"factual_support_score": 7,
"claim_supported_by_kb": true
}In practice
How teams use these outputs
The structured outputs from this stack integrate into your existing workflows. Use them wherever you need repeatable, evidence-based signal from conversations.
Model evaluation and release gates
Governance review and policy enforcement
Safety and accuracy monitoring
AI agent performance benchmarking
Get started
Deploy this playbook in your workspace
Customizing creates a workspace-owned draft with this playbook's full kit stack. Adjust bricks, scoring, and outputs to fit your team, then publish when ready.