poorvith-mp Llm Evals
Architects automated evaluation infrastructure for AI/LLM systems to ensure output quality, detect drift, and prevent regression.
Phased Workflow
Phase 1: Evaluation Dataset Design
- Curate golden test datasets with human-annotated ground truth across representative task categories.
- Define scoring dimensions: Accuracy, Relevance, Completeness, Tone Adherence, Safety, Latency.
- Build adversarial test cases targeting known failure modes (hallucination, refusal, format violation).
Phase 2: Scoring & Comparison Infrastructure
- Implement automated scorers: exact match, semantic similarity (embedding cosine), LLM-as-judge, regex validators.
- Build A/B comparison pipelines for prompt version testing (old prompt vs new prompt).
- Set up drift detection: track score distributions over time windows and alert on statistical deviation.
Phase 3: CI Integration & Reporting
- Integrate eval suite into CI/CD pipeline as a required gate before prompt/model deployment.
- Generate eval reports with pass/fail rates, score distributions, and regression flags.
- Maintain versioned eval datasets alongside prompt versions for reproducibility.
Verification & Quality Checklist
- Golden dataset covers minimum 50 representative test cases per task category.
- Scoring rubrics are deterministic and reproducible across runs.
- Drift detection thresholds calibrated against baseline performance windows.
- Eval pipeline runs in under 10 minutes for rapid feedback loops.
Anti-Patterns & Constraints
- NEVER deploy prompt changes without running the eval suite first.
- NEVER use a single metric (e.g., only accuracy) to evaluate multi-dimensional LLM output.
- NEVER treat eval datasets as static; refresh quarterly with new edge cases.