This repository offers a comprehensive tutorial on Prompt Engineering for Large Language Models (LLMs), targeting users with no technical background who want to effectively interact with AI. It provides a structured curriculum covering prompt principles, optimization techniques, and practical applications like text summarization, sentiment analysis, translation, and content generation, aiming to empower users to leverage LLMs efficiently.

How It Works

The tutorial emphasizes two core principles for effective prompting: providing clear, unambiguous instructions and guiding the model to "think" step-by-step. It advocates for using delimiters to separate instructions from content, requesting structured output (e.g., JSON, CSV), and employing "few-shot" prompting with examples. For complex tasks, it suggests breaking down the problem into sequential steps and encouraging the model to reason through the solution rather than providing a direct answer, thereby mitigating issues like AI hallucinations.

Quick Start & Requirements

• Installation: No specific installation is mentioned; the content is presented as a tutorial series.
• Prerequisites: Patience and a willingness to practice the provided examples are recommended. Access to an LLM like ChatGPT or Claude is implied for practical application.
• Resources: The tutorial is designed for users without a technical background.

Highlighted Details

• Covers foundational prompt principles, iterative optimization, and specific use cases like summarization, sentiment analysis, and content generation.
• Introduces prompt frameworks like CRISPE and a custom "Mr.Bear" framework (Input, Instruction, Output, Filter) for structured prompt design.
• Includes practical examples and explanations for common LLM behaviors like "hallucinations" and strategies to mitigate them.
• Offers guidance on translating and adapting prompts across different languages and tones.

Maintenance & Community

• The repository is maintained by "PandaBearLab" and "Mr. Bear".
• Links to the author's blog and GitHub are provided.

Licensing & Compatibility

• The content is marked as "exclusive copyright." Reproduction requires contacting the author.

Limitations & Caveats

• The tutorial acknowledges that the "LLM Basic Principles" section is incomplete.
• It notes that some domestic models may have unstable outputs, recommending ChatGPT or Claude for more reliable results.
• The author emphasizes that practical, hands-on experience is crucial for mastering prompt engineering, beyond just reading the material.