> tpl_air_020
GenAI Evaluation and Regression Suite Specification
Automated generative AI evaluation and regression testing specification establishing curated golden test datasets, LLM-as-a-Judge calibrated rubrics, Ragas retrieval metrics (faithfulness, answer relevance, context recall), CI/CD deployment gates, and statistical regression assertions.
CI/CD automated evaluation specification establishing golden datasets, LLM-as-a-Judge rubrics, and regression test gates.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Engineers tweak prompts, change temperature, or upgrade foundation model versions in production without automated regression tests, silently breaking structured JSON outputs, introducing hallucinations, and degrading user experience.
When to Use
- •Building automated CI/CD quality gates for LLM applications and agentic prompts (GitHub Actions / GitLab CI)
- •Configuring LLM-as-a-Judge evaluation pipelines using Promptfoo, DeepEval, or Ragas frameworks
- •Establishing statistical regression thresholds for faithfulness, context precision, toxicity, and schema adherence
When NOT to Use
- •For classical deterministic software unit tests and mock testing (use standard Jest/Vitest frameworks)
- •For high-level business model feasibility canvases (use TPL-AIM-015)
5 Template Sections & Structural Outline
Defining the 4-tier evaluation pyramid: Unit assertions (JSON regex, word count), Semantic similarity (BERTScore, embeddings), LLM-as-a-Judge rubrics (G-Eval), and Human expert audits.
Authoring rigorous judge prompts, few-shot examples, and chain-of-thought grading rubrics. Mitigating judge biases (positional bias, verbosity bias, self-enhancement bias).
Implementing standard Ragas metrics: Faithfulness (measuring factual grounding against context), Answer Relevance, Context Recall, and Context Precision.
Embedding Promptfoo or DeepEval tests into pull request workflows. Automatically executing evaluations on prompt file changes and reporting score diffs in PR comments.
Formal protocol for certifying new foundation model releases (e.g. upgrading from gpt-4o-2024-05-13 to gpt-4o-2024-08-06) across the full regression suite before production cutover.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
GenAI Evaluation and Regression Suite Specification - Worked Case Study
Fictional Entity: Enterprise Customer Support AI Copilot Engineering Team
Real-world production case study demonstrating complete operational adoption for Enterprise Customer Support AI Copilot Engineering Team.
- •Built Promptfoo automated CI/CD pipeline evaluating 340 golden customer queries on every Git push, preventing 14 regression incidents
- •Configured Ragas faithfulness evaluation maintaining 97.4% grounding accuracy across 45,000 product documentation pages
- •Established zero-cost regression testing environment utilizing synthetic user simulation to validate prompt safety before deployment
Frequently Asked Questions
Why can classical unit testing frameworks (Jest/PyTest) not evaluate generative AI applications effectively?
Classical software tests rely on deterministic equality assertions (e.g. output === expected). LLMs generate non-deterministic, semantically diverse natural language text. Testing GenAI requires probabilistic semantic similarity, LLM-as-a-Judge evaluators that grade adherence to multidimensional rubrics, and statistical confidence intervals over multiple runs.
How does an LLM-as-a-Judge evaluator work without introducing subjectivity?
To minimize subjectivity, the judge model is provided with a rigorous Chain-of-Thought (CoT) scoring rubric that defines explicit criteria for each score (e.g. 1 to 5). It requires the judge to extract facts, cite evidence from the context, articulate its reasoning step-by-step, and output structured JSON containing both numerical score and justification. Running with temperature 0.0 ensures reproducibility.
What are the four core RAG evaluation metrics defined by Ragas?
The four foundational RAG metrics are: (1) Faithfulness (measures if the answer is strictly grounded in the retrieved context without hallucination), (2) Answer Relevance (measures if the answer addresses the user prompt), (3) Context Precision (measures if signal-to-noise ratio in retrieved chunks is high), and (4) Context Recall (measures if all necessary ground-truth facts were retrieved).
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Authoritative Sources
- Ragas: Automated Evaluation of Retrieval Augmented GenerationExploding Gradients • OFFICIAL REQUIREMENT
- Promptfoo: Test and evaluate LLM output quality and securityPromptfoo • OFFICIAL REQUIREMENT
- EU Artificial Intelligence Act: Article 15 Accuracy, Robustness and CybersecurityEuropean Parliament and Council • OFFICIAL REQUIREMENT