Design, structure, and optimize prompts for Large Language Models.
npx skills add https://github.com/an8079/take-skills --skill prompt-engineering-an8079
Please help me install this Agent Skill. Skill: prompt-engineering Source: https://github.com/an8079/take-skills/tree/main/skills/prompt-engineering Command: npx skills add https://github.com/an8079/take-skills --skill prompt-engineering-an8079
This Skill addresses the challenge of crafting effective prompts for Large Language Models (LLMs) to ensure desired outputs and improve AI performance.
Use the prompt-engineering skill to generate a code generation prompt for a TypeScript function.
High-intent search queries and answers about installing and using this skill.
To optimize LLM prompts, you need a framework for designing and structuring prompt elements, applying techniques like Chain-of-Thought and ReAct, and conducting A/B testing to refine accuracy and relevance.
The best way to structure a code generation prompt is to use specific templates that guide the language model with clear instructions and context, ensuring the desired output format and logic are accurately generated.
Chain-of-Thought improves language model performance by structuring prompts to guide the AI through intermediate reasoning steps, resulting in more accurate and reliable outputs for complex tasks.
Yes, you can use prompt engineering for API design tasks by leveraging structured templates to guide the language model in generating consistent and well-formatted API specifications.
A/B testing for prompt evaluation is necessary to empirically measure and compare the performance of different prompt variations, allowing you to iteratively refine prompts for improved LLM outputs.
QBall-IncStandardize sub-agent invocations with a 4-part prompt structure and F# pipeline notation.
BoraPerusicEvaluate LLM outputs with automated metrics and human judgments.
steipeteTranscribe audio files into text using the OpenAI Whisper API.
icyzhCompile declarative language model calls into self-improving pipelines with automated optimization.
