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ChatGPT prompt engineering for developers

by Various

Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.

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ChatGPT prompt engineering for developers

Added 1 June 2026

Overview

A short course from DeepLearning.AI that teaches developers how to engineer prompts for ChatGPT. It covers effective prompting techniques and how to use LLMs for summarizing, inferring, transforming, and expanding text.

Best for

Best for
Developers new to LLMs who want a structured, hands-on introduction to prompt engineering

Use cases

  • Learning to craft precise prompts for code generation and debugging
  • Building applications that summarize or transform text via LLMs
  • Understanding how to structure prompts for inference and expansion tasks

Notes

A short course from DeepLearning.AI that teaches developers how to engineer prompts for ChatGPT. It covers effective prompting techniques and how to use LLMs for summarizing, inferring, transforming, and expanding text.

Use cases

  • Learning to craft precise prompts for code generation and debugging
  • Building applications that summarize or transform text via LLMs
  • Understanding how to structure prompts for inference and expansion tasks

Pros

  • Taught by Andrew Ng and OpenAI researchers, providing authoritative guidance
  • Concise and practical, focusing on developer-relevant use cases
  • Free to audit, making it accessible for self-paced learning

Cons

  • Assumes basic familiarity with Python and APIs, not for complete beginners
  • Limited depth; covers fundamentals rather than advanced optimization
  • Course content may become outdated as LLM capabilities evolve rapidly

Indexed from awesome-generative-ai and enriched against its public facts.

Pros

  • Taught by Andrew Ng and OpenAI researchers, providing authoritative guidance
  • Concise and practical, focusing on developer-relevant use cases
  • Free to audit, making it accessible for self-paced learning

Cons

  • Assumes basic familiarity with Python and APIs, not for complete beginners
  • Limited depth; covers fundamentals rather than advanced optimization
  • Course content may become outdated as LLM capabilities evolve rapidly