Testing AI products is fundamentally different from testing traditional software. You can't write a test that says "assert output === expected" because the output is probabilistic. You need new approaches.

Types of AI Evaluation

1. Automated Evaluation

• Accuracy metrics — Precision, recall, F1 for classification tasks
• Quality metrics — BLEU, ROUGE for text generation (limited but useful as baselines)
• LLM-as-judge — Use a separate AI model to evaluate the primary model's outputs
• Regression testing — Run a fixed set of inputs periodically and flag significant changes

2. Human Evaluation

• Expert review — Domain experts assess output quality
• User testing — Real users attempt real tasks with the product
• A/B testing — Compare model versions with real traffic
• Annotation — Trained annotators rate outputs on multiple dimensions

3. Red Teaming

• Adversarial testing — Try to make the system produce harmful or incorrect outputs
• Edge cases — Test with unusual, ambiguous, or malformed inputs
• Bias testing — Check for discriminatory patterns across demographics
• Safety testing — Verify guardrails work under pressure

Building an Evaluation Pipeline

1. Create a golden dataset — 100-500 input-output pairs covering common use cases, edge cases, and failure modes
2. Define rubrics — Clear criteria for what constitutes a good/bad output
3. Automate what you can — LLM-as-judge for scalable evaluation
4. Human-evaluate what you can't — Regularly review a sample of real outputs
5. Track metrics over time — Dashboard showing quality trends

Evaluation Dimensions

For most AI products, evaluate on:

• Correctness — Is the output factually accurate?
• Relevance — Does it address the user's actual question/need?
• Completeness — Does it cover all important aspects?
• Harmlessness — Does it avoid harmful, biased, or inappropriate content?
• Tone — Is the style appropriate for the context?
• Latency — Is the response fast enough for the use case?

Common Testing Mistakes

• Testing only on "happy path" inputs
• Using training data for evaluation (data leakage)
• Relying solely on automated metrics without human review
• Not testing for bias across demographics
• Ignoring latency and cost in evaluation