ChatGPT Prompt Engineering
1.0.0
Welcome to the ChatGPT Prompt Engineering for Developers repository! This repository contains Jupyter notebooks from the free course by DeepLearning.AI and OpenAI, co-taught by Isa Fulford and Andrew Ng. The course provides a practical guide to using Large Language Models (LLMs) like ChatGPT through prompt engineering techniques to build powerful applications.
The following Jupyter notebooks are included, each demonstrating a key aspect of prompt engineering:
1-guidelines.ipynb)This notebook introduces the foundational guidelines for creating effective prompts that help the LLM produce desired outputs. It covers two essential principles for prompt engineering.
2-iterative.ipynb)Learn the iterative process of refining prompts through experimentation and adjustment, leading to improved and more accurate responses from the model.
3-summarizing.ipynb)This notebook shows how to use LLMs for summarization tasks, such as condensing long pieces of text like user reviews into shorter, more digestible summaries.
4-inferring.ipynb)Explore how to use LLMs to infer insights from text, including sentiment analysis and topic extraction from user input.
5-transforming.ipynb)Learn how to leverage LLMs to transform text, such as translating languages, correcting grammar, or adjusting text style.
6-expanding.ipynb)In this notebook, you'll see how LLMs can automatically generate content, such as composing emails or generating creative text based on minimal input.
7-chatbot.ipynb)Explore how to use the chat-based format effectively in applications, building chatbots that maintain context and handle multi-turn conversations.
This repository is distributed under the MIT License. Feel free to use and modify the materials for your own projects.
This repository is based on the "ChatGPT Prompt Engineering for Developers" course. The content is subject to updates as the field of prompt engineering evolves.
