Evaluation Methodology
A repeatable, engineering-led evaluation workflow designed to uncover failure boundaries, quantify regression risks, and produce actionable remediation for production AI systems.
Version 2.0 · Tri-Modal Rubrics · Continuous Regression Defense Loop
Define
Formulate formal task definitions, input schemas, required output structure, and explicit pass/fail criteria. All evaluation rubrics and deterministic assertions are locked in before looking at candidate outputs to ensure unbiased measurement.
Prepare
Curate, clean, balance, and version-control evaluation datasets. We intentionally mine hard-negatives, out-of-distribution inputs, and edge-case boundary conditions to stress-test systems where real-world failures happen.
Run
Execute candidate models inside isolated sandboxes or private VPC harnesses. Parameters including temperature, random seeds, top-p, and context windows are strictly controlled, with full input-output request telemetry logged.
Analyze
Score outputs using our tri-modal grading system (deterministic unit assertions, calibrated model judges, and human domain experts). Failures are categorized into concrete taxonomies: retrieval omissions, reasoning loops, schema violations, or hallucinations.
Improve
Translate empirical failure analysis into engineering remediation: prompt tuning, chunking adjustments, tool parameter schema revisions, or targeted fine-tuning datasets for supervised alignment.
Re-Evaluate (Regression Defense)
Execute the frozen evaluation suite against updated model checkpoints. This guarantees that remediations addressing specific errors do not cause silent degradation across previously passing capabilities.
Code execution in Docker, regex schema matching, JSON/SQL parse tests, and exact numeric assertions. Zero grader subjectivity.
Few-shot rubrics for semantic alignment, factual groundedness, and tone conformity. Continuously cross-calibrated against human consensus.
Software engineers, legal researchers, and domain specialists audit borderline cases, prompt injections, and complex multi-hop outputs.
Locked task definitions, assertion logic, scoring guidelines, and baseline configurations.
Standardized JSONL dataset with inputs, reference ground-truths, and categorized edge cases.
Complete request/response traces, token counts, latency bounds, and step-level agent tool logs.
Metric summaries, baseline comparisons, failure taxonomy breakdowns, and engineering remediation guidance.
Packaged test harness and scripts for continuous CI/CD evaluation on subsequent model updates.
Implement Regression Defense for Your AI
Schedule an evaluation consultation with our engineering team under mutual NDA.