AI大神吴恩达与OpenAI官方合作推出的ChatGPT提示工程课，到底在讲什么？

Overview

This course focuses on best practices for developing directive-tuned language models (LMs) to build applications like chatbots.

Key Highlights

• LMs enable rapid software application development, with APIs facilitating quick builds.
• Distinguishing between basic LMs and directive-tuned LMs, with the latter being more practical.
• The importance of defining the desired tone and content when using LMs to build applications.
• Attention to detail and clarity in descriptions is crucial when creating LM-based applications.
• Acknowledgment of contributions from OpenAI and Deep Learning AI teams who supported the course.

Prompt Engineering Principles

The video teaches guidelines for writing effective prompts to enhance model performance on data.

• Setting clear APIs, managing API keys, and defining endpoints for text segmentation.
• Providing clear instructions, using separators, and avoiding rapid injections to guide the model's output.
• Structuring outputs in formats like HTML or JSON to guide the model to correct results.
• Allowing the model thinking time by increasing input, diversity, and rewriting prompts.
• Minimizing hallucinations by tracing answers back to source documents.

Iterative Prompt Engineering

The video discusses the importance of iteration in building effective prompts for large language models.

• Machine learning models require constant iteration for effectiveness.
• Efforts to make prompts shorter, clearer, and focused on key information and specific roles.
• Trying different prompts and adjusting output lengths to control results.
• Good processes and incremental improvements are necessary for quick engineering.
• Summarizing texts is a common use of large language models in software applications.

Summary and Inference Applications

One of the most exciting applications of large language models is summarizing texts, which can be used in chats, e-commerce sites for reviewing customer comments, and generating department-specific summaries.

Inference applications involve using large language models for analytical tasks, offering speed advantages over traditional machine learning workflows and simplifying complex natural language processing tasks.

Transformation Applications

Large language models are employed for transformation applications like multilingual translation, tone adaptation, and text proofreading.

• Models can translate text into multiple languages and assist with text proofreading and grammar correction.
• ChatGPT prompt engineering can transform inputs into different formats, such as from JSON to HTML.

Expansion Applications

The video introduces how to use large language models for expansion applications like generating emails and customizing responses based on sentiment, with temperature parameters influencing the variety of model responses.

Building Chatbots

Shows how to build custom chatbots using large language models, which can play different roles and remain highly customizable.

• Chatbots can be created with OpenAI's chat completion components.
• System messages allow chatbots to assume different roles, changing behavior and responses.

Course Conclusion

The engineering course teaches the basics of developing chatbots using large language models, emphasizing the responsible use of these technologies.

• Key principles of motivation and iterative rapid development were covered.
• The functionalities of large language models for reasoning and augmentation were explored.
• Participants were encouraged to gain experience through small projects and to explore the positive impacts of language models.