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What is ChatGPT prompt engineering for developers?

This is a short online course that teaches developers how to write effective prompts for large language models like ChatGPT. Created by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI, the course covers practical techniques for getting better results from LLMs, including how to chain multiple API calls together, automate repetitive tasks, and build custom chatbot applications. The course is aimed at software developers who want to integrate language models into their projects but lack experience with prompt design. It focuses on hands-on learning through examples rather than theory alone.

Key Features

Prompt writing principles

Learn techniques like being specific, providing context, and structuring requests for clearer outputs

API integration

Understand how to call the ChatGPT API from code and handle responses programmatically

Prompt chaining

Combine multiple LLM calls in sequence to break down complex tasks

Text transformation tasks

Apply prompts to summarising, translating, tone adjustment, and format conversion

Chatbot development

Build a simple conversational application using prompt engineering techniques

Practical exercises

Work through code examples in a Jupyter notebook environment to practise concepts

Pros & Cons

Advantages

  • Created by OpenAI and DeepLearning.AI, so the content reflects current best practices from the creators themselves
  • Short duration makes it accessible alongside full-time work; can be completed in a few hours
  • Hands-on coding examples mean you learn by doing, not just watching lectures
  • Free to access, removing cost barriers for developers wanting to learn prompt engineering

Limitations

  • Focuses specifically on ChatGPT; techniques may require adaptation for other language models
  • Short course means limited depth on advanced topics like fine-tuning or complex multi-step workflows
  • Requires existing programming knowledge; not suitable for complete beginners to coding

Use Cases

Adding chatbot functionality to a web application or mobile app

Automating customer support workflows by routing queries to appropriate handlers

Building internal tools that summarise documents, extract information, or translate content

Experimenting with LLM capabilities before committing to a larger implementation

Training team members on how to use language models effectively in production systems