introduction to the AIGPT Prompt Engineering Course for Developers

Welcome to this AIGPT prompt engineering course for developers. This course will share prompt best practices, focusing on using APIs to call Large Language Models (LLMs) for quickly building software applications, and exploring the possibilities and best practices of LLM APIs in various application domains.

I. Course Content Framework

1. Prompt best practices for software development
2. Coverage of common use cases
3. Hands-on session: Building a chatbot using LLMs

II. Types and Characteristics of LLMs

(A) Base LLMs

• Training objective: Predict the next word based on text training data, trained on massive data from the internet and other sources.
• Capabilities:
• Limitations: Higher risk of generating harmful content (e.g., toxic output), with fewer practical application scenarios.

(B) Instruction-Tuned LLMs

• Training process:
• Core advantages:
• Course focus: This course will focus on best practices for instruction-tuned LLMs.

III. Key Principles for Prompting LLMs

(A) Clarity, Specificity

• Case illustration:If requesting “Write something about Alan Turing,” further clarify the direction, such as:

(B) Give LLMs Time to Think

(This principle will be elaborated in the next video.)

IV. Acknowledgments

Thanks to the teams at OpenAI and DeepLearning.ai for their contributions to the course materials.