ChatGPT Prompt Engineering for Developers: Best Practices and Applications
Welcome to the exciting world of AI and prompt engineering! In this course, “ChatGPT Prompt Engineering for Developers,” we’re thrilled to have Isa Fulford joining us. As a member of the technical team at OpenAI, Isa has played a pivotal role in developing the widely used ChatGPT Retrieval plugin and has dedicated much of her efforts to educating users on how to effectively integrate Large Language Model (LLM) technology into their products. Her contributions to the OpenAI Cookbook also provide valuable insights into prompting techniques.
Understanding the Power of LLMs
The internet is filled with resources on prompting, often showcasing popular articles like “30 Prompts Everyone Needs to Know.” However, much of this content focuses on utilizing the ChatGPT web interface for specific, one-off tasks. In contrast, there is immense potential for developers to leverage LLMs as powerful tools through API calls, enabling rapid software development. My team at AI Fund, a sister company to DeepLearning.ai, has collaborated with numerous startups to explore innovative applications of LLM APIs, showcasing their ability to facilitate swift and effective development.
Course Overview
This course aims to illuminate the various possibilities available to you as a developer, as well as best practices for implementing them effectively. Here’s a brief outline of what you can expect to learn:
- Best practices for prompting in software development
- Common use cases such as summarizing, inferring, transforming, and expanding
- Step-by-step guidance on building a chatbot using an LLM
By the end of this course, we hope to ignite your creativity and inspire you to develop your unique applications.
Types of LLMs
In the realm of LLMs, we can categorize them into two primary types: base LLMs and instruction-tuned LLMs. A base LLM is trained to predict the next word based on a vast array of text data. For instance, if prompted with “Once upon a time there was a unicorn,” it might continue with a description of a magical forest inhabited by other unicorns. However, if you asked, “What is the capital of France?” the base LLM may respond with a series of related questions instead of the straightforward answer.
On the other hand, instruction-tuned LLMs are specifically designed to follow directives. For example, when asked about the capital of France, an instruction-tuned LLM will likely respond with, “The capital of France is Paris.” These models undergo additional training that involves fine-tuning based on inputs and outputs that adhere to instructions, often utilizing reinforcement learning from human feedback (RLHF) to enhance their usability and safety.
Best Practices for Instruction-Tuned LLMs
Given the advancements in instruction-tuned LLMs, much of the current practical application is shifting in their favor. While some general prompting best practices may still apply to base LLMs, we recommend focusing on instruction-tuned LLMs for most applications, as they are designed to be more user-friendly and aligned with ethical standards.
In this course, we will specifically address best practices for using instruction-tuned LLMs. It’s essential to think of giving instructions to a smart assistant who may not be familiar with the specific details of your task. For example, if you request a write-up about Alan Turing, clarifying whether you want to focus on his scientific contributions, personal life, or historical significance will lead to better results. Additionally, specifying the desired tone—be it professional or casual—can guide the LLM in generating content that meets your expectations.
Moving Forward
As we progress through the course, Isa will share valuable insights on how to provide clear and specific instructions, a crucial aspect of effective prompting. You’ll also learn the importance of allowing the LLM time to process and respond thoughtfully. So, let’s dive into the next lesson and unlock the full potential of LLMs together!
