AI Eval Design and Iteration

In traditional software, inputs and outputs are defined. In AI, inputs and outputs are fuzzy. Evals (evaluations) are the "unit tests" for AI products. They allow you to move from "vibes-based" development to metric-driven iteration. By building a rigorous "quiz" for your model, you can determine exactly how capable your product is and where it requires human-in-the-loop scaffolding.

The Eval Workflow

1. Identify "Hero Use Cases"

Don't start with generic benchmarks (like MMLU). Instead, define the specific "hero" scenarios your product must master.

• Identify the 10–20 most common or high-value queries users will give your model.
• For each query, define what a "Perfect/Gold" answer looks like.
• Include edge cases where you expect the model to struggle (e.g., complex reasoning or specific formatting).

2. Design the "Quiz" (The Eval)

Create a set of tests to gauge how well the model knows the subject material.

• Input: The specific prompt or instruction.
• Reference: The "Gold" standard answer or a set of criteria (e.g., "Must mention X," "Must not exceed 200 words").
• Scoring Mechanism: Use a more powerful model (like O1 or GPT-4o) to grade the output of your production model based on your criteria.

3. Apply the "Hill Climbing" Process