ChatGPT Prompt Engineering for Developers

Isa Fulford and Andrew Ng demonstrate prompting through Python notebook examples. The short course moves from instruction design and iteration into summarization, inference, text transformation and a chatbot. It is an application-oriented introduction: the useful comparison is with other coding courses, rather than courses concerned only with a chat interface.

What you will learn

• Write and revise prompts in a Python notebook.
• Apply a language model to summarization and text transformation.
• Follow the construction of a simple conversational application.

Who this suits

The provider calls this beginner-friendly but expects basic Python knowledge. Choose it if you want to see how an application calls a model and how the response changes when instructions change.

Before you enroll

API libraries and model names can change after a lesson is recorded. Compare example code with current provider documentation, and set a small usage budget if you reproduce it in your own account.

The public course page advertises free access during the platform beta. An accomplishment and graded assignment are associated with the separate PRO plan. Confirm current availability; free study access does not imply a free paid-plan credential.

Put it into practice

Use a short fictional review dataset to compare two summarization prompts. Check whether the summary preserves negative feedback and avoids adding facts, then save the notebook with your observations.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at DeepLearning.AI.

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