LLM Evaluation Framework
Prompt and response quality evaluation workflow for LLM iteration.
LEF
LLM Eval Framework
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Architecture flow
1 layersProblem
Prompt improvements are hard to trust when changes are judged manually and regressions are not measured across a consistent test set.
My Role
Designed the evaluation loop, validator structure, prompt comparison flow, and report format.
Solution
A prompt-versioned pipeline runs outputs through model-graded scoring, deterministic validators, and structured report generation.
Stack
Case study
Problem
Prompt improvements are hard to trust when changes are judged manually and regressions are not measured across a consistent test set.
Proof signal
Prompt A/B tests · Hallucination checks · Structured eval reports
My Role
Designed the evaluation loop, validator structure, prompt comparison flow, and report format.
Core product work
- Prompt A/B tests
- Hallucination checks
- Report generation
Solution
A prompt-versioned pipeline runs outputs through model-graded scoring, deterministic validators, and structured report generation.
A prompt-versioned evaluation pipeline runs model outputs through self-reflection scoring, deterministic validators, and report generation so changes can be compared like an MLOps monitoring workflow.
Architecture Highlights
Prompt-versioned test runs
Model-graded and rule-based evaluation checks
Structured report output for comparison
Challenges and Tradeoffs
- Avoiding false confidence from model-graded evaluation.
- Choosing deterministic checks that match real product risk.
Impact / Outcome
- Made prompt iteration more systematic by comparing changes across repeatable checks.
- Reduced reliance on manual review alone by combining validators with model-graded scoring.
Learnings
- LLM quality work needs regression testing, not just better prompts.
- Evaluation reports are more useful when tied to prompt versions.
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