Prompt Evaluation

Introduction

Prompt evaluation means scoring a prompt against a test set of inputs and expected outputs. It's how you know a prompt is better, not just different.

Beginner analogy: Like unit tests for prompts. Without them you're shipping vibes.

Understanding the topic

Core concepts to understand:

• Build a test set: 20–200 representative inputs with golden outputs.
• Score by: exact match, semantic similarity, LLM-as-judge, human rating.
• Track pass rate, hallucination rate, format adherence.
• Tools: Promptfoo, Braintrust, LangSmith, Helicone, Patronus.

Syntax reference

Visual workflow / architecture:

test_set.json
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Run prompt against each
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Score with judge / regex / human
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Report: 87% pass, 5% hallucinated, 8% format-broke

Real-world use

No serious AI team ships prompts without evals. It's the dividing line between hobby and production.

Best practices

• Build the eval set BEFORE the prompt.
• Include adversarial & edge cases.
• Track scores in CI; block regressions.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Why eval prompts?
• Q2. How build a test set?
• Q3. What is LLM-as-judge?
• Q4. Name 2 eval tools.