Prompt Hallucination Risk Calculator

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Fill the fields and press Calculate to generate your report.

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Inputs

Adjust values to match your prompt and deployment context.

Prompt title

Short label used in reports.

Prompt clarity 4

Higher clarity reduces risk.

Ambiguity level 2

Higher ambiguity increases risk.

Context completeness 70%

Missing context drives guessing.

Grounding sources provided

Docs, citations, or quoted evidence.

Verification step included

Self-check, cross-check, or tool validation.

Retrieval / tool strength

Strong retrieval reduces hallucination.

Temperature 0.6

Higher temperature increases variance.

Top-p 0.9

Lower top-p usually improves factuality.

Max tokens

Long outputs can amplify drift.

Output structure strictness

Stricter format reduces hallucination.

Examples provided 1

Good examples anchor behavior.

Constraints count 2

Do/don’t rules, scope, and time bounds.

Conflicting instructions

Conflicts trigger unreliable guesses.

Reasoning depth

Multi-step tasks raise failure probability.

Use case criticality

Higher stakes increase risk sensitivity.

Needs real-time facts

Current events increase uncertainty.

Requires numeric accuracy

Math errors can compound hallucinations.

Press Calculate to update the report above.

Example dataset

These sample rows show how inputs map to different risk bands.

Scenario	Clarity	Ambiguity	Context%	Grounding	Verification	Temp	Top-p	Structure	Criticality	Final risk
Policy Q&A with citations	5	1	85	Yes	Yes	0.2	0.7	JSON	High	~18
Creative brainstorming	3	4	40	No	No	1.2	0.95	Freeform	Low	~70
Support reply with KB retrieval	4	2	70	Yes	Yes	0.6	0.9	Bulleted	Medium	~40
Medical summary without sources	4	3	55	No	No	0.5	0.9	Structured	High	~85
Finance report, realtime requested	4	3	60	Yes	No	0.7	0.9	Structured	High	~72

Values are illustrative and depend on your specific model and tooling.

Formula used

This calculator estimates a likelihood score, then applies a stakes multiplier.

BaseLikelihood = 100 × Σ(wᵢ × fᵢ)
FinalRisk = min(100, BaseLikelihood × ImpactMultiplier)

fᵢ is a normalized risk factor from 0 to 1.
wᵢ weights sum to 1.00 across all drivers.
ImpactMultiplier combines criticality, real-time needs, and numeric sensitivity.

Use the “Top risk drivers” list to see what influenced your score most.

How to use this calculator

Enter a prompt title to label the run.
Set clarity, ambiguity, and context to match your prompt.
Choose whether you provide grounding sources and verification.
Adjust generation settings like temperature, top-p, and max tokens.
Select your structure, examples, and constraint strength.
Set stakes: criticality, real-time facts, and numeric accuracy.
Press Calculate risk to show results above.
Download a PDF report or export your session history as CSV.

Interpretation guidance

Low (0–34): Good controls; still validate key facts.
Medium (35–59): Add constraints and a verification pass.
High (60–79): Require grounding and stricter output formats.
Critical (80–100): Redesign prompt, add retrieval, or gate deployment.

Why hallucination risk needs quantification

Hallucination is rarely random; it follows prompt and deployment choices. This calculator converts those choices into a 0–100 score so teams can compare prompts and guardrails using one yardstick. Scores map into four bands: Low (0–34), Medium (35–59), High (60–79), and Critical (80–100). A stable rubric supports reviews, release gates, and audit trails without requiring retraining.

Likelihood drivers captured by the inputs

The likelihood component is a weighted sum of 14 normalized factors (0 to 1) whose weights total 1.00. Clarity and ambiguity each carry 10%, while missing grounding also carries 10% because unsupported claims are a common failure mode. Temperature contributes 10% and top‑p 6% because sampling randomness can amplify uncertainty. Structure strictness contributes 8%, and context gap adds 8% because incomplete briefs invite guessing.

Impact multipliers for deployment stakes

After likelihood, the calculator applies an impact multiplier that reflects how costly an error would be. Criticality scales the score by 1.00 for low‑stakes, 1.15 for medium, and 1.30 for high. Real‑time fact requirements add 1.10 because knowledge can be stale, and numeric sensitivity adds 1.05 because arithmetic slips can cascade. The final score is capped at 100 to keep interpretations consistent.

Actionable thresholds that reduce risk quickly

Use the top driver list to target the biggest contributors first. Moving retrieval from basic to strong reduces its risk factor from 0.6 to 0.2, often lowering the score without rewriting the whole prompt. Switching from freeform to JSON reduces the structure factor from 1.0 to 0.2 by enforcing a schema. For factual tasks, dropping temperature from 1.2 to 0.4 cuts its normalized risk from 0.6 to 0.2 and typically improves consistency.

Tracking improvement across iterations

Each calculation can be stored in session history (up to 50 runs) and exported as CSV for analysis. Keep the prompt title consistent, change one control at a time, and compare “before” and “after” runs to quantify the effect of grounding, verification, or stricter formatting. Over time, the exported dataset can support trend charts, policy compliance checks, and prompt library governance.

FAQs

What does the final risk score mean?

It estimates the chance of unsupported or incorrect output, adjusted for your stakes settings. Use it to compare prompt versions and decide which mitigations to add before deployment.

How should I set context completeness?

Estimate how much of the needed facts, constraints, and definitions the prompt includes. If the model must assume missing details, lower the percentage. If you provide full specs, examples, and references, raise it.

When should I lower temperature and top‑p?

Lower them for factual, compliance, or numerical work where consistency matters. Higher randomness is better for ideation, but it raises hallucination likelihood when the task expects a single correct answer.

Is strong retrieval always required?

Not always. For self‑contained tasks with complete context, basic retrieval may be enough. For policies, product facts, or large knowledge bases, strong retrieval plus citation requirements materially lowers the risk score.

How do I reduce loose structure risk?

Provide a strict template, schema, or JSON format, and require the model to fill specific fields. Add validation rules like allowed sources, time windows, and a short “unknown” option when evidence is missing.

Can I use this for different models and teams?

Yes. Keep the same inputs and scoring bands to compare prompts across models, environments, and reviewers. Treat it as a governance tool that supports consistent review, not as a guarantee of correctness.

Session history