ChatGPT prompt engineering for developers
A short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).
What is ChatGPT prompt engineering for developers?
ChatGPT Prompt Engineering for Developers is an educational short course offered by DeepLearning.AI in collaboration with OpenAI, designed to teach developers how to effectively use large language models (LLMs) through advanced prompt engineering techniques. The course covers practical strategies for crafting prompts that yield better results, automating complex workflows, chaining multiple LLM calls together, and building custom chatbot applications. Led by Isa Fulford from OpenAI and renowned AI educator Andrew Ng, this course connects LLM capabilities and practical implementation, providing hands-on experience with real-world prompt patterns and best practices. It's ideal for software developers, data scientists, and technical professionals looking to integrate LLMs into their applications efficiently and effectively.
Key Features
Prompt engineering best practices and techniques for best LLM interactions
Workflow automation strategies using LLM capabilities
LLM call chaining and composition for complex tasks
Custom chatbot development and deployment guidance
Hands-on coding examples and practical exercises
Expert instruction from OpenAI and DeepLearning.AI instructors
Pros & Cons
Advantages
- Free access to high-quality content from industry experts at OpenAI
- Practical, hands-on approach with real code examples
- Short course format allows completion in minimal time commitment
- Directly applicable techniques for production applications
- Up-to-date content from creators of ChatGPT itself
Limitations
- Limited scope compared to thorough developer programs
- Requires existing programming knowledge to fully benefit
- May not cover advanced enterprise-scale implementation scenarios
Use Cases
Building customer service chatbots and virtual assistants
Automating content generation and documentation workflows
Developing intelligent search and question-answering systems
Creating productivity tools that use LLM capabilities
Implementing multi-step reasoning systems for complex problem-solving