Prompt-Engineering-By-Google

Day 1: Prompt Engineering

Introduction

Prompt engineering is the art of designing effective prompts to guide AI models in generating desired responses. Adjusting parameters such as temperature, top-P, and top-K can significantly influence the creativity, coherence, and accuracy of model outputs.

Parameter Tuning

To fine-tune the model's response, consider the following settings:

• Balanced Output:

  • Temperature: 0.2
  • Top-P: 0.95
  • Top-K: 30
  • Yields coherent and moderately creative results.

• Highly Creative Output:

  • Temperature: 0.9
  • Top-P: 0.99
  • Top-K: 40
  • Generates more imaginative responses but may introduce hallucinations.

• Less Creative (More Deterministic) Output:

  • Temperature: 0.1
  • Top-P: 0.9
  • Top-K: 20
  • Ideal for structured and predictable outputs.

• Single Correct Answer (e.g., Math Problems):

  • Temperature: 0.0
  • Ensures consistency by eliminating randomness.

Types of Prompting

1. Zero-Shot Prompting: Providing a prompt without examples.
2. Single-Shot Prompting: Providing a single example to guide the model.
3. Few-Shot Prompting: Providing 3 to 5 examples to improve response accuracy.

Chain of Thought (CoT) Prompting

Chain-of-Thought (CoT) prompting instructs the model to generate intermediate reasoning steps before delivering a final answer. This approach enhances logical coherence and improves performance on complex reasoning tasks.

Key Points about CoT:

• Works best when combined with few-shot examples.
• Helps the model produce more structured reasoning.
• Increases token usage, leading to higher computational costs.
• Does not fully eliminate hallucinations but improves reliability.

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Feel free to contribute by sharing insights and improvements!

🚀 Happy Prompting!