Google's 68-page ultimate prompt engineering guide

[Diego Mendoza]

This whitepaper by Lee Boonstra provides a comprehensive guide to prompt engineering for large language models (LLMs) like Gemini. It covers essential techniques such as zero-shot, few-shot, system/role/contextual prompting, Chain of Thought (CoT), Tree of Thoughts (ToT), and ReAct (Reason & Act). The document also explores best practices like using structured outputs (e.g., JSON), controlling model configurations (temperature, top-K, top-P), and documenting prompt iterations.

Key takeaways:

• Techniques: Use step-by-step reasoning (CoT), role assignment, or external tools (ReAct) for complex tasks.
• Best Practices: Be specific in prompts, prefer instructions over constraints, and experiment with formats/styles.
• Challenges: Avoid repetition loops, hallucinations, and token limits by tuning configurations.
• Automation: Tools like Automatic Prompt Engineering (APE) can streamline prompt creation.

Ideal for developers and non-technical users alike, this guide emphasizes iterative testing and documentation to refine prompts effectively.

For more details, check out the full whitepaper here:

You do not have permission to view the full content of this post. Log in or register now.

Happy prompting!

 

[Boy Mason]

Support ts

 

[Diego Mendoza]

Support ts

Thank you po

 

[hilyas]

Cool!

 

[Dunamiss]

Thanks bro, appreciate the share

 

[nmDak]

Thanks boss

 

[Tixij]

Thanks TS! Keep on sharing and more power!