DeepLearning.AI
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Free course on DeepLearning.AI

ChatGPT Prompt Engineering for Developers

1 hours 4.8(142,000 reviews)Free

At a glance

Rating
Rating 4.8
Hours
1
Cost
Free

No certificate with this course.

Open Course

DeepLearning.AI's Prompt Engineering for Developers course teaches you to use the ChatGPT API to build practical applications (summarizers, classifiers, question-answering systems, and chatbots) in about 2 hours. It is free on deeplearning.ai and was built with OpenAI. You need basic Python to follow along. This is for developers building AI applications, not for casual users looking for ChatGPT tips.

DeepLearning.AI's courses have been taken by over 6 million learners across its platform and Coursera, according to the company's about page (source: deeplearning.ai/about/).

About This Course

A hands-on short course from DeepLearning.AI and OpenAI. Learn to use LLMs to build powerful applications. Covers best prompt engineering practices, summarising, inferring, transforming text, and chatbots. Taught by Andrew Ng. Completely free.

Course Details

Duration
1h
Certificate
No
Cost
Free
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Frequently Asked Questions

Is DeepLearning.AI's Prompt Engineering course free?

Yes. The course is free on deeplearning.ai with no subscription required. You do not need a Coursera account to access it.

Do I need to know Python to take this course?

Yes. The examples use Python to call the OpenAI API. You need basic Python (variables, functions, loops) to follow the code. Take a Python basics course first if you are not comfortable yet.

How long does this course take?

About 2 hours for the video lectures and exercises. It is a short, focused course rather than a full certification program.

What can I build after taking this course?

You will know how to structure prompts for summarization, classification, inference, transformation, and chatbot dialogue using the ChatGPT API. That gives you the foundation to add AI features to Python applications.

Is this course for beginners or advanced learners?

Intermediate. It assumes basic Python and some familiarity with what large language models are. It is not a deep dive into how models work; it is a practical course on how to use them through an API.

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