The Eval-Driven Development Playbook for AI Products

If you're building AI products without a robust evaluation framework, you're flying blind. Eval-driven development is the practice of defining measurable quality criteria before writing a single prompt, then iterating based on systematic evaluation rather than gut feeling.

Why Evals Matter

Traditional software has tests. AI products need evals. The difference is subtle but important:

• Tests check for exact correctness: does add(2, 3) return 5?
• Evals measure quality on a spectrum: is this summary good enough?

Without evals, every prompt change is a gamble. You might improve one case while breaking ten others.

Building Your First Eval Suite

Start simple. You need three things:

1. A golden dataset — 50-100 representative inputs with expected outputs
2. A scoring function — automated metrics like BLEU, ROUGE, or custom rubrics
3. A baseline — your current model's scores to compare against

def evaluate(model, dataset):
    scores = []
    for example in dataset:
        output = model.generate(example.input)
        score = score_output(output, example.expected)
        scores.append(score)
    return sum(scores) / len(scores)

The Eval-First Workflow

Before making any change to your AI system:

1. Run evals on the current version (baseline)
2. Make your change
3. Run evals again
4. Compare results — ship only if quality improves or stays flat

This feels slow at first but saves enormous time debugging regressions later.

Key Metrics to Track

| Metric | What it measures | |--------|-----------------| | Accuracy | Correctness of outputs | | Latency | Response time (p50, p95) | | Cost | Token usage per request | | Hallucination rate | Frequency of fabricated information |

The best AI teams track all four and set alerts when any degrades.