eval-driven-development

Write evaluations before changing LLM prompts or models.

29|8|Updated Jul 5, 2026

Search Tags:#prompt engineering #LLM #test-driven development #eval-driven development #model adjustment

Ready to run

npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill eval-driven-development-contextjet-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-driven-development
Source: https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/eval-driven-development
Command: npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill eval-driven-development-contextjet-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill ensures LLM features remain reliable by promoting an eval-first development approach, mitigating the risk of regressions through systematic testing.

Core Features & Use Cases

Eval-Driven Development: Prioritize writing evaluations before making changes to LLM prompts or models.
Baseline Scores: Establish a baseline for performance to identify regressions.
Incremental Changes: Test changes one at a time to isolate their impact.
Regression Testing: Integrate new bugs as test cases to prevent future regressions.
CI Integration: Automate eval runs in Continuous Integration for real-time feedback.

Quick Start

Trigger the eval-driven-development skill by prompting, "I need to test the reliability of my LLM feature."

Frequently Asked Questions about eval-driven-development

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

FAQPage Schema

How do I prevent prompt regressions when iterating on LLM features?▼

To prevent prompt regressions during LLM feature development, adopt an eval-first approach by writing evaluations before modifying prompts, establishing baseline scores to measure performance, and testing incremental changes to isolate their impact.

What is eval-driven development for LLM prompt engineering?▼

Eval-driven development is a systematic approach for LLM features that prioritizes writing evaluations before making prompt or model adjustments, ensuring feature reliability by mitigating the risk of regressions through systematic baseline performance testing.

How do I set up automated regression testing for LLM prompts?▼

You can set up automated regression testing for LLM prompts by integrating new bugs as test cases and automating evaluation runs in Continuous Integration, which provides real-time feedback to prevent future model regressions.

Can I use test-driven development practices for LLM model adjustments?▼

Yes, you can apply test-driven development practices to LLM model adjustments by establishing baseline performance scores first, then testing incremental changes individually to isolate their impact and ensure feature reliability.

Why does my LLM feature performance degrade after prompt updates?▼

LLM feature performance degrades after prompt updates because changes often introduce regressions; establishing baseline scores and testing incremental changes one at a time isolates their impact to mitigate this reliability risk.

What's the best way to isolate the impact of prompt engineering changes?▼

The best way to isolate the impact of prompt engineering changes is to test incremental changes one at a time against an established baseline score, ensuring feature reliability by preventing regressions.