Summary of Google Prompt Engineering Course

Feb 4, 2025

Google Prompt Engineering Course Summary

Course Overview

  • Course Structure: Four modules on prompting essentials
    1. Writing prompts like a pro
    2. Designing prompts for everyday work tasks
    3. Using AI for data analysis and presentations
    4. Using AI as a creative or expert partner
  • Emphasis on practical examples and assessment for information retention

Module 1: Writing Prompts Like a Pro

  • Definition of Prompting: Providing specific instructions to a Gen AI tool
  • Prompt Design Framework: Task, Context, References, Evaluate, Iterate (Mnemonic: "Tiny Crabs Ride Enormous Iguanas")
    • Task: Define what you want the AI to do
    • Context: The more context, the better the output
    • References: Provide examples to clarify your intention
    • Evaluate: Assess if the output meets your needs
    • Iterate: Refine the prompt for better results
  • Iteration Methods:
    1. Revise the prompt framework
    2. Break prompts into shorter sentences
    3. Try different phrasings or analogous tasks
    4. Introduce constraints
    • Mnemonic for iteration methods: "Brahma Saves Tragic Idiots"

Module 2: Designing Prompts for Everyday Work Tasks

  • Use Cases: Emails, brainstorming, building tables, summarizing documents
  • Email Writing Example: Creating a professional and friendly email
  • Importance of Tone and Context: Tailor prompts to match the desired tone
  • Prompt Library: Capture examples for future reference

Module 3: AI for Data Analysis and Presentations

  • Data Analysis Tips: Be cautious with sensitive data
  • Example Prompts:
    • Calculate average sales per customer
    • Analyze trends in data sets
  • Presentation Building: Use AI to draft and refine presentation content

Module 4: Using AI as a Creative or Expert Partner

  • Advanced Techniques:
    • Prompt Chaining: Guide AI through a series of prompts
    • Chain of Thought: Ask AI to explain its reasoning
    • Tree of Thought: Explore multiple reasoning paths
  • AI Agents:
    • Agent Sim: Simulate scenarios (e.g., interview training)
    • Agent X: Provide expert feedback
    • Designing Agents: Persona, context, interactions, stop phrases, feedback

Issues with AI Tools

  • Hallucinations: Incorrect or nonsensical outputs
  • Biases: Human-like biases in the tool's output
  • Human-in-the-Loop: Always verify AI outputs

Conclusion

  • Assessment: Reflect on learning to improve retention
  • Course Feedback: Dense content with practical examples