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Read online -> islinxu.github.io/prompt-engineering-note/

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ChatGPT Prompt Engineering Learning Notesfor Developers (ChatGPT Question Engineering Learning Notes for Developers)

The course briefly introduces how language models work, provides the best tips for engineering practices, and shows how to apply the language model API to applications for various tasks. In addition, the course provides Jupyter Notebook code examples, and you can directly use the API Key provided by OpenAI to obtain results, so you can experience it without an account.

In ChatGPT Prompt Engineering for Developers, you can learn how to quickly build powerful new applications using large language models (LLM). With the OpenAI API, you will be able to quickly build the ability to learn innovation and create value that was previously expensive, technical, or simply impossible.

This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLM works, provide best practices for instant engineering, and demonstrate how the LLM API is used for applications for a variety of tasks, including:

Summary (for example, summarizing user comments for brevity)
Inference (e.g., emotion classification, topic extraction)
Convert text (e.g., translation, spelling, and grammar corrections)
Extensions (e.g., auto-writing emails)

In this course, you can learn two key principles for writing effective tips, namely how to systematically design tips and learn to build custom chatbots .

All concepts are illustrated with numerous examples that can be used directly in the official Jupyter notebook environment for practical experience in real-time engineering.

Main content

Course Chapters

Course Introduction
Guidelines
Tip project needs iterative
Summarizing Applications (Summarizing)
Inferring Application (Inferring)
Transforming
Expanding
Create a chatbot (Chatbot)
Conclusion

This project is a compilation of study notes for ChatGPT Prompt Engineering for Developers courses. I would like to thank Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) for the wonderful courses provided by them. These contents are of great help to beginners like me. Based on the idea of learning and practice, I did the following things, hoping to help prompt engineering learners:

1. Use propt+ChatGPT to machine translate the original text of the course content (the full text is translated and generated by ChatGPT, and each chapter provides Chinese-English comparison);
2. Use propt+ChatGPT to summarize and expand the notes (at the end of each section, the effect of ChatGPT summary is attached);
3. The corresponding jupyterbook code in the practice process was sorted out, at: jb_code (can be run in a localized deployment environment);

4. Make a shell cli command script based on notebook code (updated)

python source/cli/cli_py.py --prompt " hello chatgpt " --model " gpt-3.5 "

gen_code

gen_code1

5. A list of projects related to awesome prompting projects (under update): https://islinxu.github.io/prompt-engineering-note/projects/index.html
6. Toy project: Boot ChatGPT implementation to generate tiny-gpt (implement a simple gpt model from scratch)
- Recorded in tiny-gpt
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