chatgpt promt engineer for developer

ChatGPT Prompt Engineering for Developers

This repository documents my progress and learnings from the 'ChatGPT Prompt Engineering for Developers' course by DeepLearning.AI. The course covers essential techniques for creating effective prompts for ChatGPT, focusing on different aspects such as summarizing, inferring, transforming, and more.

Overview

In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including:

• Summarizing (e.g., summarizing user reviews for brevity)
• Inferring (e.g., sentiment classification, topic extraction)
• Transforming text (e.g., translation, spelling & grammar correction)
• Expanding (e.g., automatically writing emails)

In addition, you’ll learn two key principles for writing effective prompts, how to systematically engineer good prompts, and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter notebook environment to get hands-on experience with prompt engineering.

This course uses a third-party app, ChatGPT Prompt Engineering for Developers, to enhance your learning experience. The app will reference basic information like your name, email, and Coursera ID.

Table of Contents

1. Introduction
2. Guidelines
3. Iterative
4. Summarizing
5. Inferring
6. Transforming
7. Expanding
8. Chatbot
9. Conclusion

Examples

You can find practical examples and exercises for each section by finding & executing Python code in the code or play with Jupyter Notebook in the notebook directories.

How to Run Examples

1. Install Python and Pip
2. Install Jupyter Notebook:
   • New notebook version: pip install notebook
   • Old notebook: pip install jupyterlab
3. Run Jupyter Notebook: open & change directory to this repository then run jupyter lab
4. Access the Jupyter Notebook Web UI at localhost:8888 | http://localhost:8888/lab/tree/notebook/
5. In the left-side bar, open File Browser (Ctrl/Command + Shift + F), then open the notebook for the corresponding section
6. Point to each code block, then click [Execute] on the Toolbar or press [Shift + Enter]

Additional Resources

Check out the additional resources for more reading materials and tools related to prompt engineering.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.

Contact

If you have any questions, feel free to reach out via GitHub Issues.