Prompt Engineering Outline

Prompt Engineering For Beginners Outline

Apr 30, 2025

Introduction: What is Prompt Engineering and Why It Matters

Outline:

Introduction: What is prompt engineering and why it matters
Understanding how LLMs work at a basic level (prediction engines)
Basic prompting techniques for beginners:
- Zero-shot prompting: Just ask what you want
- One-shot and few-shot: Including examples to guide the AI
- Simple instructions vs. complex prompts
Practical examples showing before/after prompt improvements
Common beginner mistakes to avoid
Tips for getting started with your first prompts
Conclusion: The iterative nature of prompt engineering

Outline:

Introduction: The balancing act between creativity and precision
What is temperature in AI models?
When to use different temperature settings:
- Low temperature (0-0.3): Factual, consistent responses
- Medium temperature (0.4-0.7): Balanced responses
- High temperature (0.8-1.0): Creative, diverse outputs
Real-world examples comparing the same prompt at different temperatures
Other sampling controls: Top-K and Top-P explained simply
How to choose the right settings for your specific needs
Conclusion: Finding your temperature sweet spot

Outline:

Introduction: Getting AI to adopt specific personas
The power of context in AI interactions
System prompting: Setting the stage for AI behavior
Role prompting techniques:
- How to assign clear roles to the AI
- Popular roles that improve specific outputs
- Examples of effective role prompts
Contextual prompting: Adding background information
A step-by-step guide to creating your first role-based prompt
Conclusion: Expanding your prompting toolkit with personas

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Introduction: The problem with direct questions and answers
What is Chain of Thought (CoT) prompting?
When to use reasoning chains:
- Mathematical problems
- Logic puzzles
- Complex decision-making
Simple examples contrasting direct questions vs. CoT approach
How to construct effective reasoning prompts
Real-world applications for everyday users
Conclusion: Getting AI to show its work improves results

Outline:

Introduction: The challenge of getting consistent AI outputs
Why structure matters in AI responses
Techniques for requesting specific formats:
- JSON and structured data
- Lists and tables
- Step-by-step instructions
Example prompts that enforce structure
How to specify output length and detail level
Common formatting issues and how to fix them
Conclusion: Better prompts lead to more usable outputs

Outline:

Introduction: Moving beyond basic prompting
Best practices from Google’s white paper:
- Be specific about desired outputs
- Use instructions over constraints
- Experiment with different formats
- Document your prompt attempts
The iteration process: How to improve prompts systematically
Creating a personal prompt library for reuse
Tools to help track and improve your prompts
Conclusion: Becoming a better prompt engineer through practice

Outline:

Introduction: The expanding world of AI inputs
What is multimodal prompting?
Types of inputs AI can now understand:
- Text and images together
- Code with explanations
- Visual problem-solving
Simple examples anyone can try
Use cases for everyday multimodal prompting
Tips for effective multimodal interactions
Conclusion: The future of AI communication

Outline:

Introduction: How AI is changing programming workflows
Prompt techniques specific to code generation:
- Writing new code from descriptions
- Explaining existing code
- Debugging and reviewing
- Translating between languages
Real examples of coding prompts and outputs
Best practices for programming-related prompts
Limitations to be aware of
Conclusion: Integrating AI into your development process

Would you like me to elaborate on any of these outlines or suggest different angles for introducing prompt engineering to beginners?