KB article
Evaluation: How to Test AI Answers Against Your Model
Evaluation compares AI answers against expected model outputs to detect errors.
arf-kbai-readiness-interoperabilitydeterministic-querysemantic-contractretrieval-context
TL;DR
- Evaluation is essential for trust.
- Use gold‑set queries for validation.
The problem
- 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
- 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.
Framework placement
Primary ARF layer: AI Readiness & Interoperability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability, change-reliability.