Evaluate LLM quality by testing whether the application completes its intended tasks correctly and safely. Use a versioned task set, explicit scoring rules, and the same conditions for every candidate. A single model benchmark or judge score cannot establish quality for your product.
Create a representative evaluation set
- Sample real task categories with permission and remove unnecessary personal information.
- Include ordinary requests, ambiguous questions, unsupported requests, and high-impact failures.
- Write the expected outcome and what counts as failure before running the candidates.
- Keep a held-out set so repeated prompt tuning does not become the whole evaluation.
| Dimension | Useful check | Failure example |
|---|---|---|
| Task completion | Does the required action or answer match the rubric? | A support answer omits the escalation path |
| Grounding | Do accessible sources support the material claims? | A citation exists but says something different |
| Authorization | Does the application enforce the user's permissions? | A tool returns another customer's record |
| Reliability | Repeat trials and record timeouts and invalid outputs | The happy path works only intermittently |
| Cost and latency | Measure complete successful and failed attempts | Retries erase the apparent per-call saving |
Make scoring auditable
Use deterministic checks for exact formats and application state. Use qualified human review for judgments that need expertise. If an LLM grades responses, record the judge model, prompt, and disagreement with human labels. Keep failures in the dataset rather than dropping inconvenient examples.
Compare versions fairly
Pin model identifiers where supported, prompt versions, tool permissions, retrieval snapshots, and evaluation settings. Run repeated trials when outputs vary. Report counts alongside rates and separate high-impact failures from minor style issues. A difference on a small sample may not justify a rollout.
Use results in production decisions
Choose release criteria for the actual workflow, record tradeoffs, and monitor representative outcomes after rollout. Re-evaluate when the model, prompt, retrieval corpus, tools, or traffic changes. This worksheet does not claim an independent ranking of model providers.
References and further reading
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.