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
   • Summarization
   • Reasoning
   • Transformation
   • Expansion
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:
  • Can 续写文本 (e.g., prompting "Once there was a unicorn" might generate "and all its unicorn friends lived in a magical forest").
  • Can answer factual questions (e.g., "What is the capital of France?"), but may produce errors.
• Limitations: Higher risk of generating harmful content (e.g., toxic output), with fewer practical application scenarios.

(B) Instruction-Tuned LLMs

• Training process:
  1. First train a base LLM on massive text data;
  2. Fine-tune and optimize it using instructions, input-output examples;
  3. Further refine it through "Reinforcement Learning from Human Feedback (RLHF)" to make it more helpful and instruction-following.
• Core advantages:
  • More aligned with human instructions (e.g., clearly answering "The capital of France is Paris");
  • Higher safety, lower likelihood of generating harmful content than base LLMs;
  • Have become the mainstream choice for current practical applications.
• 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:
  • Focus area: scientific work, personal life, historical role, etc.;
  • Tone and style: professional news article, brief essay, etc.;
  • Reference materials: specify text passages to read for writing about Alan Turing.

(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.