FG-0607 Structured Model Evaluation
Description
Head-to-head model comparison using consistent prompt sets, automated metrics, and structured JSON results. Multiple candidates are evaluated against the same benchmarks, scored on defined dimensions, and the winner advances. This is model selection through controlled experiment, not vibes. Every evaluation produces a reusable artifact: the prompt set, the scoring rubric, and the results archive.
Implementation
Build a reusable evaluation runner. Define your prompt set covering the dimensions that matter for your use case. Score each candidate on consistent axes: accuracy, brevity, structure, methodology, factual correctness. Automate what you can (word count, format compliance, canary leak detection, token throughput). Record results as structured JSON. Run the same eval on every candidate. Winner advances, losers are documented with specific failure modes.
Success Indicators
- Model selection decisions are backed by reproducible evaluation data
- Evaluation prompt sets grow and improve with each round
- Results are structured and comparable across candidates and time periods
- The team can articulate why the current model was chosen with specific metrics
Failure Modes
- Evaluating on too few prompts, producing statistically meaningless results
- Scoring dimensions that do not reflect real-world use, optimizing for the wrong thing
- Not automating: manual evaluation does not scale and introduces inconsistency
- Single-round evaluation without re-testing as models update and capabilities shift
Sub-Methods 3
Build a prompt set that covers the dimensions you care about. Include easy baseline prompts, domain-specific challenges, edge cases, and adversarial inputs. The prompt set is a reusable asset. Version it. Grow it after each evaluation round based on what you learned.
Automate every metric you can: word count, format compliance, token throughput, canary leak detection, response time. Manual scoring for subjective dimensions (quality, methodology) but automate the rest. The scoring pipeline should produce a structured JSON result per candidate per prompt.
Structure evaluations as tournament rounds. Winner of each round advances to face the next challenger. Document why each loser was eliminated with specific failure modes. The tournament archive becomes a decision log for model selection history.