Microsoft AI Evaluations

Key Takeaways

The Microsoft AI Evaluations Library provides eight built-in evaluators for automated LLM response quality checks. Integrate directly into CI/CD pipelines for scalable, consistent assessments. Groundedness evaluates context support; Relevance checks intent alignment.

Overview

The Microsoft.Extensions.AI.Evaluation library is an open-source .NET framework that automates quality checks for AI-generated responses. Deploy evaluators as CI/CD gates to catch quality regressions before production.

Evaluators Reference

Evaluator	Metric	What It Checks
RelevanceEvaluator	Relevance	Response addresses user query/intent
TruthEvaluator	Truth	Factual correctness against ground truth
CompletenessEvaluator	Completeness	All required aspects covered
FluencyEvaluator	Fluency	Grammar, vocabulary, readability
CoherenceEvaluator	Coherence	Logical flow and organization
RetrievalEvaluator	Retrieval	Context retrieval effectiveness
EquivalenceEvaluator	Equivalence	Similarity to reference output
GroundednessEvaluator	Groundedness	Response grounded in provided context

Interview Angle

“How do you evaluate AI response quality in a .NET pipeline?” — Explain the distinction: Relevance ensures the response answers the right question; Groundedness ensures it only uses provided context (critical for retrieval-augmented generation). Both matter, but they measure different things.

Why Use This

• Automated quality gates — Catch regressions before production; integrate into CI/CD workflows
• CI/CD-ready — Available as NuGet package: Microsoft.Extensions.AI.Evaluation.Quality
• Customizable — Extend interfaces to build domain-specific evaluators
• LLM-powered consistency — Scalable assessments without manual review bottlenecks

Resources

📚 Microsoft Evaluations Documentation

Common Gotchas

Evaluators use LLMs (costs apply per call). Groundedness requires context to be explicitly provided — without it, scores are meaningless. No single metric is a silver bullet; combine multiple evaluators for comprehensive assessment.