This repository provides Jupyter notebooks for the "ChatGPT Prompt Engineering for Developers" course by DeepLearning.AI and OpenAI. It targets developers and researchers seeking to leverage Large Language Models (LLMs) via the OpenAI API for building powerful applications, offering practical examples and best practices for prompt engineering.

How It Works

The course material focuses on practical application of LLMs through prompt engineering. It covers core concepts of how LLMs function, systematic prompt design principles, and API integration for tasks like summarization, inference, text transformation, and content expansion. The approach emphasizes hands-on learning within a Jupyter notebook environment.

Quick Start & Requirements

• Install dependencies via pip install -r requirements.txt.
• Requires Python 3.x and an OpenAI API key.
• Access to Jupyter Notebook or a compatible environment.
• Official course page: https://www.deeplearning.ai/courses/chatgpt-prompt-engineering-for-developers/

Highlighted Details

• Hands-on Jupyter notebooks for practical prompt engineering.
• Covers summarization, inference, text transformation, and expansion tasks.
• Teaches systematic prompt engineering and building custom chatbots.
• Course taught by experts from OpenAI and DeepLearning.AI.

Maintenance & Community

This repository is a static collection of course materials. Community interaction and support are likely to be found on the DeepLearning.AI or OpenAI platforms rather than directly on this GitHub repository.

Licensing & Compatibility

The repository does not explicitly state a license. The content is derived from a course by DeepLearning.AI and OpenAI, implying potential usage restrictions tied to their terms of service. Commercial use and linking with closed-source projects should be verified against the course provider's policies.

Limitations & Caveats

The repository contains course materials and does not represent a production-ready library or framework. Users will need to manage their own OpenAI API access and associated costs. The effectiveness of prompt engineering is highly dependent on the specific LLM and task.