ChatGPT Prompt Engineering for Developers
A hands-on short course on prompt engineering best practices and building LLM-powered applications with the OpenAI API.
About
ChatGPT Prompt Engineering for Developers is a beginner-friendly short course created by DeepLearning.AI in collaboration with OpenAI. Taught by Isa Fulford and Andrew Ng, it introduces practical prompt engineering techniques for application development and demonstrates how large language models can be used for summarization, inference, text transformation, expansion, and chatbot development. The course includes interactive examples and hands-on practice with the OpenAI API.
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Learning Outcomes
- Understand core prompt engineering principles for application development
- Write clearer and more effective prompts
- Systematically iterate on prompts
- Use LLMs to summarize text
- Use LLMs for classification and information extraction
- Transform text through translation and editing workflows
- Expand short inputs into longer generated content
- Build a simple custom chatbot using an LLM API
Skills Covered
Prompt Engineering, Large Language Models, OpenAI API, LLM application development, Text summarization, Text classification, Information extraction, Text transformation, Content expansion, Chatbot development, Prompt iteration
Syllabus
- Introduction
Introduces the course, large language models, and the role of prompt engineering in application development.
- Guidelines
Covers core principles and best practices for writing effective prompts.
- Iterative Prompt Development
Shows how to systematically improve prompts through testing, evaluation, and refinement.
- Summarizing
Uses LLMs to summarize longer text into concise outputs.
- Inferring
Covers tasks such as sentiment classification, topic identification, and information extraction.
- Transforming
Applies LLMs to translation, rewriting, spelling correction, grammar correction, and related transformations.
- Expanding
Demonstrates how short prompts can be expanded into longer generated responses such as emails.
- Chatbot
Shows how conversational prompts and API calls can be combined to build a custom chatbot.
- Conclusion
Reviews the main prompt engineering techniques and development patterns covered in the course.
Prerequisites
- Basic Python knowledge recommended
- No advanced machine learning knowledge required
Target Audience
Developers, Prompt engineers, Software engineers, AI beginners with basic Python knowledge, Machine learning practitioners, Professionals building LLM-powered applications
Best For
- Developers learning practical prompt engineering
- Learners following an AI Developer learning path
- Learners following a Prompt Engineer learning path
- Developers experimenting with LLM APIs
- Professionals who want hands-on prompt engineering practice
Pros
- Short and highly focused
- Taught by instructors from DeepLearning.AI and OpenAI
- Hands-on coding examples
- Covers several common LLM application patterns
- Includes systematic prompt iteration
- Suitable for beginners with basic Python knowledge
Cons
- Requires some basic Python familiarity for the coding examples
- Focused on introductory application patterns rather than advanced LLM engineering
- Does not provide deep coverage of production deployment, evaluation infrastructure, or advanced agent systems
Course Facts
- Provider:
- DeepLearning.AI
- Instructor:
- Isa Fulford and Andrew Ng
- Duration:
- 1 hour 30 minutes
- Level:
- Beginner
- Language:
- English
Certification & Delivery
Certificate Available
Format: Online, Self-paced, Video lessons, Interactive code examples
Tools you can use with this course
ChatGPT
A conversational AI model developed by OpenAI that excels at answering questions, writing code, and generating creative content.
- This course directly references the tool
- The tool category matches this course
Blackbox AI
Blackbox AI is a powerful coding assistant that allows developers to convert natural language into code and provides intelligent code completion.
- The tool category matches this course
Codeium
Codeium provides lightning-fast AI autocomplete and an intelligent chat interface to help developers write, debug, and refactor code efficiently.
- The tool category matches this course
Cursor
An AI-powered code editor built on top of VS Code, designed to boost developer productivity through intelligent code generation and refactoring.
- The tool category matches this course
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Prompt Engineer Learning Path
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Related Glossary Terms
API
An API, or Application Programming Interface, is a defined way for software systems to communicate and exchange requests, data, or functionality.
Large Language Model (LLM)
A large language model is an AI model trained on large amounts of text and other data to understand and generate language by predicting and producing sequences of tokens.
Prompt Engineering
Prompt engineering is the systematic process of designing, testing, evaluating, and refining instructions given to generative AI systems in order to produce more useful, reliable, and appropriately structured outputs.
System Prompt
A system prompt is a high-level instruction provided to an AI model that defines behavior, role, constraints, priorities, or operating rules for an interaction.
Token
A token is a unit of text or other input that an AI model processes, such as part of a word, a whole word, punctuation, or another encoded element.