Optimize and validate LLM prompts with A/B testing and metric tracking.
npx skills add https://github.com/rbaumier/skills --skill prompt-engineering-rbaumier
Please help me install this Agent Skill. Skill: prompt-engineering Source: https://github.com/rbaumier/skills/tree/main/prompt-engineering Command: npx skills add https://github.com/rbaumier/skills --skill prompt-engineering-rbaumier
Prompt engineering tackles the challenge of getting high-quality outputs from LLMs by crafting and refining prompts that guide models to reliable, efficient results.
Run an initial prompt optimization pass on a given prompt and report the best variation.
High-intent search queries and answers about installing and using this skill.
You can optimize LLM prompts by running automated passes that test variations, apply guardrails, and validate outputs against metrics to improve reliability and efficiency.
Yes, A/B testing is supported for system prompts and few-shot configurations, allowing you to track metrics and validate which prompt variations perform best.
The best way to reduce token usage is applying optimization strategies to prompt templates, which streamlines instructions and supports caching to lower operational overhead.
You can debug a prompt template by running deterministic testing and validation to identify failures, applying guardrails, and refining the instructions to improve output reliability.
Yes, the prompt optimization tooling requires the numpy dependency to run its scripts for automated prompt validation and metric tracking.
You need to build a reusable prompt library when managing multiple projects, allowing you to speed up development by standardizing validated system prompts and templates.
ricardorocheAutomate prompt engineering with structured templates, few-shot examples, and injection prevention.
Adam-GuerinOptimize LLM prompts with few-shot learning and chain-of-thought techniques.
coval-aiAnalyze human versus LLM judge annotation discrepancies to refine evaluation prompts.
tarunccetDesign production-quality prompts with output schemas and guardrails for structured outputs.
