AI Readiness & Interoperability
Evaluation: How to Test AI Answers Against Your Model
Evaluation compares AI answers against expected model outputs to detect errors.
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Test questions yield AI and expected answers, compared for a score.
TL;DR
- • Evaluation is essential for trust.
- • Use gold‑set queries for validation.
The problem (layman)
- • Teams deploy AI without a validation process.
- • Errors are discovered only by users.
Why it matters
- • Evaluation prevents silent failures.
- • It supports continuous improvement.
Symptoms
- • AI answers differ from known report values.
- • No baseline for accuracy.
Root causes
- • No gold‑set query library.
- • Lack of evaluation tools.
What good looks like
- • A library of test questions and expected answers.
- • Regular evaluation runs with reporting.
How to fix (steps)
- • Define a gold‑set of questions.
- • Compare AI outputs to model results.
- • Track accuracy trends over time.
Pitfalls
- • Testing only easy questions.
- • Ignoring context differences.
Checklist
- • Gold‑set defined.
- • Evaluation runs regularly.
- • Results reviewed and acted on.