prompt-engineering

prompt-engineering

Purpose

This skill enables OpenClaw to craft and optimize prompts for AI models, improving output quality, accuracy, and efficiency in tasks like text generation, classification, or code completion. It uses techniques such as chain-of-thought prompting and few-shot learning to fine-tune interactions with LLMs.

When to Use

Use this skill when AI outputs are suboptimal, such as vague responses, hallucinations, or poor performance in NLP tasks. Apply it for model fine-tuning, debugging AI behavior, or integrating prompts into applications like chatbots or automated content generators.

Key Capabilities

• Generate prompt templates with variables (e.g., {input_text}) for dynamic reuse.
• Optimize prompts using metrics like perplexity or response length via built-in analyzers.
• Support for popular models like GPT-4 or BERT through adapters.
• Iterative refinement: Automatically suggest variations based on feedback loops.
• Integration with embedding services for semantic similarity checks using the provided embedding hint.

Usage Patterns

Always start with a base prompt and iterate: 1) Define the prompt structure, 2) Test with sample inputs, 3) Analyze outputs, 4) Refine using optimization flags. For CLI, chain commands like openclaw prompt create followed by openclaw prompt test. In code, wrap prompts in functions for modular reuse. Use JSON config files for complex setups, e.g., specify "model": "gpt-4" and "temperature": 0.7.