Develop, optimize, and evaluate LLM prompts using Python, Markdown, and YAML.
npx skills add https://github.com/fanguyun/SkillManager --skill prompt-engineer-fanguyun
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/fanguyun/SkillManager/tree/main/prompt-engineer Command: npx skills add https://github.com/fanguyun/SkillManager --skill prompt-engineer-fanguyun
This Skill streamlines the creation, optimization, and evaluation of prompts for Large Language Models (LLMs), enhancing performance across various application scenarios.
Run the prompt-engineer skill with the following command: prompt-engineer generate-template --template "refine existing prompt" --input "original_prompt.txt" --output "optimized_prompt.txt" --evaluation "test_prompt.txt".
High-intent search queries and answers about installing and using this skill.
To optimize and evaluate prompts for LLMs, you can generate templates that refine existing prompts and implement evaluation frameworks to measure model performance. This addresses diverse scenarios like few-shot learning and structured outputs.
Designing LLM prompts for new applications involves focusing on accuracy, efficiency, and context management. The best way utilizes various prompting techniques including zero-shot, few-shot, and chain-of-thought to achieve structured outputs.
Prompt engineering requires Python for scripting and YAML for configuration, along with markdown for documentation. These dependencies support the development, optimization, and evaluation of prompting techniques.
To improve token efficiency when refining existing LLM prompts, you apply optimization techniques that focus on context management and structured outputs. This enhances overall model performance across various application scenarios.
You need few-shot learning and chain-of-thought prompting techniques when developing prompts for complex LLM applications requiring high accuracy. These methods optimize performance by guiding the model's reasoning process.
You can evaluate LLM model performance against a set of test prompts by implementing evaluation frameworks. This measures accuracy and efficiency by running the model through designated test prompt files.
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