Part 2 of a series on testing AI systems in production

In Part 1, we explored why testing AI systems is fundamentally different from traditional software.

We talked about non-determinism, prompt sensitivity, and why unit tests aren’t enough.

Now let’s move from theory to practice.

How do you actually build a system to test AI reliably?

This post walks through a practical approach to building an AI evaluation pipeline—from dataset creation to CI/CD integration.

What is an AI Evaluation Pipeline?

At a high level, an evaluation pipeline looks like this:

Dataset → System → Evaluation → Metrics → Analysis

More concretely:

• You define a dataset of test cases
• Run them through your AI system
• Evaluate outputs using defined metrics
• Store and analyze results over time

This becomes your source of truth for system quality.

Step 1: Build a High-Quality Evaluation Dataset

Your evaluation pipeline is only as good as your dataset.

Where data comes from:

• Production logs (most valuable)
• Synthetic examples (for coverage)
• Edge cases and failure scenarios

Example structure:

{
  "input": "What is the refund policy?",
  "expected": "Answer should mention 30-day refund window",
  "context": "Optional (for RAG systems)",
  "metadata": {
    "type": "faq",
    "difficulty": "easy"
  }
}

What makes a good dataset:

• Represents real user behavior
• Includes edge cases
• Covers known failure modes

Insight: Most teams underestimate this step. Dataset quality matters more than model choice in many cases.

Step 2: Define Evaluation Metrics

Unlike traditional systems, correctness isn’t always binary.

You’ll need a mix of evaluation strategies.

Common approaches:

1. Exact match (for structured tasks)

• Useful for classification or JSON outputs

2. Semantic similarity

• Measures meaning, not exact wording

3. LLM-as-a-judge

• Uses a model to evaluate output quality

4. Task success (for agents)

• Did the system complete the objective?

Tradeoffs:

• Exact match → precise but brittle
• Semantic → flexible but fuzzy
• LLM judge → scalable but imperfect

The key is combining multiple signals.

Step 3: Run Evaluations

At this stage, you execute your system against the dataset.

A simple evaluation loop might look like this:

results = []

for sample in dataset:
    output = system.run(sample["input"])

    score = evaluator(
        output=output,
        expected=sample.get("expected"),
        context=sample.get("context")
    )

    results.append({
        "input": sample["input"],
        "output": output,
        "score": score
    })

Keep it simple at first. Complexity can come later.

Step 4: Store Results and Enable Debugging

Raw scores are not enough. You need visibility.

Store:

• Inputs
• Outputs
• Scores
• Metadata

Add:

• Failure tagging
• Error categories (hallucination, formatting, etc.)
• Trace logs (especially for agents)

This is what allows you to answer:

Why did the system fail?

Without this layer, debugging becomes guesswork.

Step 5: Track Changes Over Time

An evaluation pipeline is not a one-time exercise.

You should be able to answer:

• Did the latest change improve performance?
• Did hallucination rates increase?
• Did a prompt tweak break edge cases?

Track metrics like:

• Accuracy
• Hallucination rate
• Task success rate

Version your datasets and compare results across runs.

Step 6: Integrate with CI/CD

This is where evaluation becomes part of engineering discipline.

Run evaluations when:

• Prompts change
• Models are updated
• Retrieval logic is modified

Example workflow:

Code Change → Run Evals → Compare Metrics → Pass/Fail

You can define thresholds like:

• Fail if accuracy drops below X%
• Fail if hallucination rate increases

This prevents silent regressions.

End-to-End Flow

Putting it all together:

Dataset
   ↓
Run System
   ↓
Evaluate Outputs
   ↓
Store Results
   ↓
Compare with Previous Runs
   ↓
Trigger Alerts / Decisions

This is your AI quality control loop.

Real-World Example

Let’s say you’re testing a support chatbot.

Before pipeline:

• Manual testing
• Inconsistent results
• Hard to track improvements

After pipeline:

• ~200 real queries as dataset
• Automated evaluation on every update
• Clear metrics (correctness, grounding)

Outcome:

• Faster iteration
• Reduced hallucinations
• Better confidence in releases

Common Pitfalls

Even with a pipeline, teams run into issues:

• Overfitting to the evaluation dataset
• Blind trust in LLM-as-a-judge
• Not updating datasets with real usage
• Lack of dataset versioning

Avoid treating evals as static—they should evolve with your system.

What’s Next

In the next part of this series, I’ll go deeper into:

• Evaluating RAG systems (retrieval + generation)
• Measuring context relevance and faithfulness
• Common failure patterns in retrieval pipelines

Final Thoughts

AI systems don’t fail loudly—they drift.

An evaluation pipeline gives you a way to detect, measure, and control that drift.

It’s not just about testing once.
It’s about building a system that continuously tells you:

Is my AI still working as expected?