Prompt engineering for large language models like Gemini. It covers configuring output settings like token limits, sampling controls, and prompting techniques. The goal is to craft prompts and settings that guide the AI to consistently produce desired outputs.

Several techniques improve LLM performance, including few-shot learning, system prompting, step-back prompting, chain-of-thought reasoning, self-consistency, tree-of-thought reasoning, and REACT. These techniques enhance accuracy, creativity, and reliability by providing context, guiding reasoning, and enabling external tool use.

Prompt engineering is an iterative process of experimenting, learning, and refining prompts. Best practices include providing examples, keeping prompts simple and specific, using instructions over constraints, and documenting the process. Techniques like chain-of-thought, self-consistency, and REACT enhance LLM reasoning and problem-solving.

Effective communication with AI tools involves understanding building blocks and experimenting. Further resources, including Google’s prompting guides and research papers, are available for those interested in exploring this field.