ChatGPT Prompt Engineering for Developers: A short course from OpenAI and DeepLearning.AI

What You Will Learn

• How to build powerful applications using large language models (LLMs) through API access
• Best practices for developers to create effective prompts for LLMs
• How to apply LLMs to common use cases such as summarizing, inferring, transforming, and expanding text

Key Concepts

• Prompt Engineering: The process of designing and optimizing prompts to get the best results from large language models.
• Large Language Models (LLMs): Powerful AI models that can understand and generate human-like language, enabling a wide range of applications.
• API Access: The ability for developers to access and interact with LLMs programmatically, allowing for integration into custom applications.
• Common Use Cases: Specific tasks that LLMs can be applied to, such as summarizing long texts, inferring meaning from text, transforming text into different formats, and expanding short prompts into longer outputs.
• Custom Chat Bots: Applications that use LLMs to generate human-like responses to user input, enabling interactive and engaging user experiences.

Code Examples

Unfortunately, there are no code snippets provided in the transcript that can be used as examples.

Lesson Summary

In this lesson, we introduced the concept of prompt engineering for developers, which involves designing and optimizing prompts to get the best results from large language models (LLMs). We discussed how LLMs can be used to build powerful applications through API access, and explored common use cases such as summarizing, inferring, transforming, and expanding text. We also touched on the idea of using LLMs to build custom chat bots, which can enable interactive and engaging user experiences. The goal of this lesson is to spark your imagination about the many applications that can be built using LLMs and to provide a foundation for learning how to build them. By the end of this course, you can expect to have a good sense of what applications can be built on top of LLMs and how to go about building them.

Practice Exercise

Try to think of a specific application or use case where you could apply a large language model to solve a problem or improve a process. Write down a brief description of the application and the type of prompt you would use to interact with the LLM. Consider how you could use the LLM to summarize, infer, transform, or expand text in your application.

What Is Next

In the next lesson, we will dive deeper into the specifics of prompt engineering, exploring techniques and best practices for designing effective prompts that get the best results from LLMs. We will also start to explore the technical details of how to interact with LLMs using APIs, setting the stage for building our own applications.