Structured worksheet for hallucination testing

LLM Reliability Evaluation Framework

This interactive worksheet guides enterprise AI teams through a systematic process to evaluate hallucination rates in large language models (LLMs). It includes structured inputs for test scope and data, calculators for hallucination metrics, and a result card to assess model reliability.

Evaluating hallucination rates is critical for enterprises deploying large language models in decision-support contexts. This interactive worksheet steps through defining the evaluation scope, gathering test data, and measuring hallucination performance using industry-standard metrics.

By quantifying unsupported or fabricated output across a controlled test set, buyers and platform engineering leads can benchmark model reliability, understand risk exposure, and guide oversight policies.

Inputs

Number of test samplessamples

Enter the total number of prompt-response pairs used for hallucination testing.

Number of hallucinated responses identifiedsamples

Count the number of responses that include verifiably incorrect or fabricated information.

Number of responses with partial hallucinationsamples

Count how many responses exhibit a mixture of correct and hallucinated information.

Scope of evaluation

Select the domain focus for the hallucination test.

Model name and version

Specify the LLM under test, including version number or variant.

Test methodology

Choose the approach used for hallucination detection.

Calculations

Hallucination rate

(hallucinated_samples + partial_hallucination_samples / 2) / total_test_samples * 100

6.50 %

Full hallucination rate

hallucinated_samples / total_test_samples * 100

5.00 %

Partial hallucination rate

partial_hallucination_samples / total_test_samples * 100

3.00 %

Results

LLM Hallucination Evaluation Summary

Moderate hallucination risk

The measured hallucination rate fits within recommended bounds for enterprise readiness according to Gartner's 2023 AI Reliability Report.

Best practice

Enter test samples that represent the actual intended use cases as closely as possible. Domain-specific testing is essential because hallucination rates vary widely by context and model version.

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