This repository serves as a comprehensive guide to prompt engineering techniques for effectively interacting with large language models like ChatGPT. It targets individuals seeking to improve the quality and relevance of AI-generated text, offering practical methods and structured approaches for users of all levels.

How It Works

The guide systematically breaks down various prompt engineering strategies, including instruction prompts, role prompts, zero/one/few-shot prompting, and more advanced techniques like "Let's think step by step" and self-consistency. Each technique is explained with a clear prompt formula and illustrative examples, demonstrating how to guide the model's output for specific tasks and desired outcomes.

Quick Start & Requirements

This repository is a guide, not a software package. No installation or specific requirements are needed beyond access to a large language model like ChatGPT.

Highlighted Details

• Provides structured prompt formulas for each technique.
• Includes practical examples for diverse use cases like content generation, summarization, and Q&A.
• Covers a wide range of techniques from basic instructions to advanced methods like adversarial and reinforcement learning prompts.
• Explains how to combine different prompt techniques for enhanced results.

Maintenance & Community

This repository appears to be a static compilation of prompt engineering techniques, likely derived from a book. There is no indication of active maintenance, community contributions, or associated community channels.

Licensing & Compatibility

The repository itself does not specify a license. The content is presented as a guide, implying it can be used for learning and personal application. Compatibility for commercial use or closed-source linking would depend on the original source of the information, which is stated to be a book.

Limitations & Caveats

The repository is a textual guide and does not provide any executable code or tools. Its effectiveness relies entirely on the user's ability to apply the described techniques to an actual LLM interface. The content is a translation and compilation, and the original author notes that similar work may already exist.