Day 7: Research and share prompt engineering techniques

1. Zero-shot Prompting

• How it works: Generates outputs without specific training or examples. Useful for quick answers to general questions.
• Potential impact: Allows AI to respond to novel tasks without specific coding.
• Resource: What is Zero Shot Learning in Computer Vision?

2. Few-Shot Prompting

• How it works: Provides a few examples to guide AI responses.
• Potential impact: Enables more accurate and context-specific outputs.
• Resource: Few-Shot Prompting Guide

3. Chain-of-Thought Prompting (CoT)

• How it works: Involves breaking complex tasks into smaller reasoning steps.
• Potential impact: Facilitates multi-step reasoning, improving AI’s problem-solving capabilities.
• Resource: Chain-of-Thought Prompting Guide

4. Role-based Prompting

• How it works: Assigns a role or persona to the AI.
• Potential impact: Helps guide AI to adopt specific perspectives or tones.
• Resource: Role-based Prompt Engineering

5. ReAct Prompting

• How it works: Involves breaking down tasks into a series of reasoning steps with specific actions.
• Potential impact: Useful for complex problem-solving tasks.
• Resource: ReAct Prompting

6. Self-Consistency Prompting

• How it works: Aims for consistent responses by focusing on context and coherence.
• Potential impact: Ensures outputs remain cohesive and on-topic.
• Resource: Self-Consistency Prompting Guide

7. Iterative Prompting

• How it works: Builds upon previous responses with follow-up questions.
• Potential impact: Useful for deeper exploration and clarifications.
• Resource: Iterative Prompting

8. Retrieval Augmented Generation (RAG)

• How it works: Accesses external knowledge to ground AI’s responses.
• Potential impact: Helps combat hallucinations and provides more reliable outputs.
• Resource: RAG Prompting Guide

Each technique has its unique strengths, and choosing the right one depends on the task’s complexity and desired output