This white paper offers a practical guide to getting better results from large language models like Gemini. It covers key configuration options (temperature, top-K, top-P), and breaks down prompting techniques—zero-shot, few-shot, chain-of-thought, role, system, and more.
It also explores advanced methods like ReAct and Tree-of-Thought, with real examples for reasoning, coding, and debugging. Clear best practices and real-world patterns make it a valuable resource for anyone working with LLMs in production or experimentation.