Evaluation-Driven Development

Traditional software development has unit tests. LLM development has evaluations. The principle is the same: define what "correct" means before you build.

The Problem with "Vibes-Based" Development

Many teams develop LLM applications like this:

1. Write a prompt
2. Try it a few times
3. "It looks good to me!"
4. Deploy to production
5. Hope for the best

This approach fails because:

• LLMs are non-deterministic—the same prompt can give different results
• Edge cases are hard to spot with manual testing
• Quality degrades silently over time
• No way to compare versions objectively

Evaluation-First Approach

Evaluation-driven development flips the workflow:

1. Define success criteria - What does a "good" response look like?
2. Build test cases - Create examples of inputs and expected outputs
3. Create evaluators - Write code or prompts that score responses
4. Baseline your current state - Run evaluations before making changes
5. Iterate with confidence - Make changes and measure the impact

# Example: Define what we're measuring before we build
evaluation_criteria = {
    "faithfulness": "Response must only use information from context",
    "relevancy": "Response must directly answer the question",
    "format": "Response must be under 100 words"
}

What Unit Tests Are to Code, Evaluations Are to LLMs

Software Development	LLM Development
Unit tests	Single-turn evaluations
Integration tests	Multi-turn evaluations
End-to-end tests	Full workflow evaluations
Test coverage	Dataset coverage
CI/CD gates	Evaluation thresholds

Building Your First Evaluation Dataset

Start with 10-20 high-quality examples:

1. Happy path cases: Normal, expected queries
2. Edge cases: Unusual but valid queries
3. Adversarial cases: Tricky inputs that might confuse the model
4. Failure cases: Queries the model should refuse or handle carefully

Pro tip: Add real production failures to your dataset. Every bug becomes a test case.

Continuous Evaluation

Don't just evaluate before deployment:

• On every code change: Run evaluations in CI/CD
• On schedule: Daily or weekly regression checks
• On production traffic: Sample and evaluate real requests
• On model updates: Re-evaluate when upgrading models

The Payoff

Teams using evaluation-driven development report:

• Faster iteration: Confidence to make changes quickly
• Fewer production issues: Catching problems before deployment
• Better collaboration: Shared understanding of quality standards
• Measurable progress: Clear metrics showing improvement over time

In the next module, we'll dive deep into LLM evaluation fundamentals—the techniques and patterns that make this approach work. :::