Testing AI Applications with Microsoft.Extensions.AI.Evaluation for Reliable Software
AI features don’t fail like normal features. Your code compiles, the endpoint is up, the UI looks fine—and then the model answers the same question two different ways on two different days. That’s not a “bug” in the classic sense. It’s the nature of probabilistic systems. And it’s exactly why evaluation (evals) has become the missing piece between “cool demo” and “reliable software.”
Microsoft.Extensions.AI.Evaluation is Microsoft’s attempt to make evals feel like normal .NET testing: code-first, DI-friendly, and something you can run in Test Explorer or in a pipeline without inventing an entire framework from scratch. The practical win is simple: you can treat model quality as a build artifact—measured, tracked, and gated.
- These libraries evaluate both quality (relevance, coherence, fluency, completeness, groundedness) and safety (harmful content categories, indirect attacks, etc.).
- Quality evaluators often use an LLM to score an LLM, so you need to think about judge bias and calibration.
- Built-in response caching and the dotnet aieval CLI make repeated runs cheaper and CI-friendly.
- The real shift: testing is the new prompting—quality drops should fail builds, not surprise users.
Understanding AI Evaluations
AI evaluations are structured tests that measure whether your app’s model outputs meet your expectations across scenarios you care about. If you’ve ever said “it worked yesterday,” you already understand why this matters. Evals give you a way to detect:
- Regression: a model update makes answers less relevant or less grounded.
- Prompt drift: a refactor changes instruction-following behavior in subtle ways.
- Retrieval breakage: your RAG pipeline starts citing the wrong docs or inventing detail.
- Safety slippage: content filters weaken, or the system becomes easier to manipulate.
In late 2025, evals are increasingly treated like unit tests: not perfect predictors of production behavior, but the best early-warning system you can automate.
What Microsoft.Extensions.AI.Evaluation Actually Ships
The Evaluation libraries are designed to sit on top of Microsoft.Extensions.AI abstractions (notably IChatClient), so the same “client” you use in your app can also be used as the evaluator’s scoring engine. That keeps integration friction low and makes the framework feel like it belongs in the .NET ecosystem.
- Core abstractions: build or plug in your own evaluators (via IEvaluator and metric types).
- Quality evaluators: score relevance, completeness, fluency, coherence, equivalence, groundedness, and more.
- NLP evaluators: classic similarity metrics (useful when you want a non-LLM judge).
- Safety evaluators: evaluate harmful content categories and certain attack patterns.
- Reporting + CLI: store results, cache responses, and generate HTML reports with dotnet aieval.
It’s also built to integrate with your existing testing workflow—xUnit/MSTest/NUnit locally, and dotnet test in CI. There’s explicit support for publishing results to reporting surfaces, including pipeline-friendly output and report generation.
The Evaluation Loop
The code-first pattern is straightforward: define scenarios (“golden prompts”), run them against your system, then evaluate the responses with a set of metrics. In Microsoft’s examples, you create a configuration that bundles:
- the chat client (the model under test, or the judge—depending on your setup),
- a set of evaluators (quality/safety/NLP),
- and reporting/caching behavior.
Then you run an evaluation pass, retrieve metric values, and fail the test if the score doesn’t meet your bar. That last part is the mindset shift: you’re no longer “reading outputs and vibing.” You’re enforcing thresholds.
using Microsoft.Extensions.AI;
using Microsoft.Extensions.AI.Evaluation;
using Microsoft.Extensions.AI.Evaluation.Quality;
public class RagEvals
{
[Fact]
public async Task Groundedness_should_not_fail()
{
// 1) Call your app/model to get a response (messages + response)
(IList<ChatMessage> messages, ChatResponse response, string retrievedContext) =
await CallMyRagSystemAsync("What does our refund policy say about damaged items?");
// 2) Provide grounding context explicitly for groundedness checks
var ctx = new List<EvaluationContext> { new GroundednessEvaluatorContext(retrievedContext) };
// 3) Evaluate
var evaluators = new IEvaluator[] { new GroundednessEvaluator(), new CoherenceEvaluator() };
EvaluationResult result = await EvaluationRunner.EvaluateAsync(evaluators, messages, response, ctx);
// 4) Gate
var grounded = result.Get<NumericMetric>(GroundednessEvaluator.GroundednessMetricName);
Assert.False(grounded.Interpretation!.Failed);
}
}The important pattern is not the syntax—it’s the loop: run → score → gate.
The “Judge LLM” Debate
A lot of late-2025 evaluation is “AI judging AI.” That’s powerful, but it’s not neutral. If your quality evaluators use an LLM to score relevance or coherence, you’re introducing a second model with its own preferences and failure modes.
In practice, the mature approach looks like this:
- Use multiple evaluator types: combine LLM-based quality scoring with classic NLP similarity checks where you have reference answers.
- Calibrate with humans: spot-check borderline cases and adjust rubrics or thresholds.
- Stabilize evaluation inputs: keep temperature low and prompts deterministic for judge calls when possible.
- Track judge drift: if you change the judge model/provider, treat that as a versioned dependency that can shift scores.
Your application model is “production.” Your judge is “instrumentation.” If you don’t trust your instrumentation, you’re not measuring—you’re guessing.
CI/CD: Eval-Gating Instead of Shipping Surprises
The place evals pay for themselves is CI/CD. You run the same golden scenarios on every build (or on every model/prompt change), and you block deployment if a critical metric drops below your threshold.
Two late-2025 patterns make this realistic:
- Response caching: repeated evaluations can reuse cached responses when prompts and models are unchanged, keeping pipelines from ballooning in time and cost.
- Reporting automation: after tests run, you can generate an HTML report via dotnet aieval and attach it as a build artifact for review.
- Gate on a small set of “must-not-regress” metrics (e.g., groundedness for RAG, tool-call accuracy for agents).
- Warn (don’t fail) on softer metrics like fluency unless your domain demands it.
- Version your eval suite like any other test asset. Treat changes as code reviews, not casual edits.
Practical Scenarios That Actually Match Real Work
1) Legal-tech RAG where groundedness is non-negotiable
You don’t want “helpful.” You want “provably sourced.” This is where groundedness checks and retrieval scoring matter, and where you pass the retrieved context into the eval so the scorer can detect when the response invents details.
2) Customer-service bots where brand voice coherence matters
Coherence and fluency are nice, but the real failure is voice drift: a bot that suddenly sounds sarcastic, overly formal, or inconsistent with policy language. Here, coherence scoring plus reference-style NLP checks can catch changes early.
3) Tool-using agents that must follow instructions
Agent-focused metrics (like task adherence, intent resolution, and tool-call accuracy) are designed for the modern “AI does steps” world. They’re especially useful when agents become pipelines—multiple calls, multiple tools, multiple chances to go off-rail.
Challenges in Testing AI Systems
Even with a strong framework, evals are still hard. The pain points haven’t disappeared; they’ve just become measurable:
- Non-determinism: you may need multiple runs or stricter sampling settings for stability.
- Coverage: a tiny eval suite can be gamed by accident; expand gradually and keep it representative.
- Cost and time: evals are model calls; caching helps, but you still need a budget.
- False confidence: passing a metric is not a guarantee—just a better signal than vibes.
Conclusion: Toward Reliable AI Software
Microsoft.Extensions.AI.Evaluation pushes .NET AI development toward a familiar discipline: if you can’t test it, you can’t ship it responsibly. The most successful AI applications in 2025 aren’t the ones with the cleverest prompts—they’re the ones with evaluation suites that detect regressions early and enforce quality as a deploy-time contract.
Testing is the new prompting. Treat models as software components. Make them earn their way into production.
FAQ: Tap a question to expand.
▶ What is the purpose of AI evaluations?
Evaluations measure how reliably an AI system performs across scenarios, helping detect regressions, drift, and unsafe behavior before users experience it.
▶ Does Microsoft.Extensions.AI.Evaluation replace human review?
No. It reduces blind spots and speeds feedback loops, but human judgment is still needed—especially to calibrate thresholds, audit borderline cases, and validate domain correctness.
▶ How do I keep eval runs from slowing down CI?
Start with a small “gating” suite, enable response caching, and expand coverage gradually. Treat evals like integration tests: targeted, meaningful, and monitored for runtime.
▶ Why is “AI judging AI” risky?
A judge model can have its own biases and drift over time. Mitigate by mixing evaluator types, calibrating with humans, and versioning your judge configuration.
References
- Microsoft.Extensions.AI.Evaluation libraries (docs)
- Put your AI to the Test (Microsoft developer blog)
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