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eval-harness

Implement Eval-Driven Development frameworks with code-based, model-based, and human graders.

1|Updated Oct 14, 2023
Search Tags:#testing#ai development#metrics#evaluation#framework#regression
One-click install
npx skills add https://github.com/walterfan/lazy-ai-primer --skill eval-harness-walterfan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/walterfan/lazy-ai-primer/tree/main/assets/skills/eval-harness
Command: npx skills add https://github.com/walterfan/lazy-ai-primer --skill eval-harness-walterfan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured framework for evaluating AI code generation and behavior, ensuring reliability and preventing regressions through Eval-Driven Development (EDD) principles.

Core Features & Use Cases

  • Capability Evals: Define and test new AI functionalities.
  • Regression Evals: Ensure existing features remain unbroken.
  • Grading: Supports code-based, model-based, and human grading.
  • Metrics: Tracks pass@k and pass^k for reliability.
  • Use Case: Before implementing a new authentication feature, define capability evals to test user registration and login. After implementation, run regression evals to ensure existing public routes are still accessible.

Quick Start

Use the eval-harness skill to define a new capability evaluation for user registration.

Frequently Asked Questions about eval-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is eval-driven development for AI and how does it prevent regressions?▼

Eval-driven development evaluates AI code generation and behavior to ensure reliability and prevent regressions by defining capability and regression evaluations before and after implementing features.

How do I set up capability and regression evaluations for AI code generation?▼

Set up capability evaluations to define and test new AI functionalities, then run regression evaluations after implementation to ensure existing features remain unbroken and accessible.

What grading methods can I use to evaluate AI behavior in a testing framework?▼

You can evaluate AI behavior using code-based graders, model-based graders, and human graders to assess AI reliability across different evaluation scenarios.

What metrics should I track to measure AI reliability during development?▼

Track pass@k and pass^k metrics to assess AI reliability, measuring the consistency and success rate of AI code generation across multiple evaluation attempts.

When should I run regression evaluations in an AI development session?▼

Run regression evaluations after implementing new AI functionalities to verify that existing features remain unbroken and accessible, preventing unintended behavior regressions.

Does this evaluation framework work without external dependencies?▼

Yes, the evaluation framework operates without external dependencies, allowing you to define and run capability and regression evaluations directly within your AI development environment.

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