Efficiently build multi-step systems using large language models.
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Instructors: Isa Fulford, Andrew Ng
Efficiently build multi-step systems using large language models.
Learn to split complex tasks into a pipeline of subtasks using multistage prompts.
Evaluate your LLM inputs and outputs for safety, accuracy, and relevance.
In Building Systems with the ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. Unlock new development capabilities and improve your efficiency in this brand new short course.
You’ll build:
You’ll learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning.
This one-hour course, taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite.
Hands-on examples make each concept easy to understand. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and prompts presented in the course.
We are thrilled to offer this course with OpenAI to support developers wanting to grow their skills using generative AI tools. This course reflects the latest understanding of best practices for getting the best performance out of LLM models, and doing so in a responsible way.
Building Systems With The ChatGPT API is beginner-friendly. You only need a basic understanding of Python to be able to complete the course. This course is also suitable for intermediate or advanced machine learning engineers who want to learn cutting-edge prompt engineering skills for LLMs.
Introduction
Language Models, the Chat Format and Tokens
Classification
Moderation
Chain of Thought Reasoning
Chaining Prompts
Check Outputs
Evaluation
Evaluation Part I
Evaluation Part II
Summary
Course access is free for a limited time during the DeepLearning.AI learning platform beta!
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