Conversational AI Prompt Engineering

Learning Objectives

• This course teaches effective conversational AI interaction, moving from basic to systematic prompting to maximize AI value.
• Students will master GPT (Generative Pre-trained Transformer) based AI systems for increased productivity, regardless of industry.

Topics in the Course

• Prompt Engineering: Definition and importance.
• Limitations of simple prompting.
• Common prompting mistakes.
• Optimizing prompts for optimal AI results.
• Using Persona patterns for improved results.
• AI model interviews.
• Chain-of-Thought prompting approach.
• Revolutionary Train of Thought technique.
• Advanced techniques, including the Nova System.

Modules

• Module 1: Introduction to Prompt Engineering:
  • Defining Prompt Engineering and its importance.
  • English as a new programming language.
• Module 2: Getting Started with Prompt Engineering:
  • Understanding GPT-like AI tools.
  • Naive prompting approach.
  • Persona pattern.
  • Interview pattern.
• Module 3: The Chain-of-Thought Approach:
  • AI reasoning and problem-solving.
  • Utilizing step-by-step reasoning for complex problems.
• Module 4: Advanced Techniques:
  • Tree-of-Thought Approach.
  • Controlling Verbosity and the Nova System.
• Module 5: Final Project:
  • Course summary.
  • Optional final project.

What is Prompt Engineering?

• Designing and refining instructions to language models (like GPT-3.5).
• Crafting input to elicit desired responses or behaviors.
• Tailoring prompts for accuracy, coherence, and alignment with specific aims.

Types of Prompting

• Zero-Shot: Task given without examples.
• One-Shot: Model given a single example.
• Few-Shot: Model given multiple examples.

Additional Information

• AI models, like GPT-3.5, have limitations in accuracy and reasoning capabilities.
• Data used to train AI models may be flawed, causing occasional errors or biases.
• Prompt engineering helps to mitigate these limitations by guiding the AI to provide more accurate and relevant results.
• There are numerous approaches to prompting, including simple methods, personas, chains of thought, and advanced methods like the "Tree of Thoughts" technique.
• Control the verbosity of the AI's responses to ensure sufficient detail for the desired output.