Prompt Example

Prompt engineering for large language models (LLMs) focuses on optimizing the input instructions to elicit desired outputs, improving performance on various tasks without extensive model retraining. Current research emphasizes efficient techniques like parameter-efficient fine-tuning and active prompting, often incorporating chain-of-thought reasoning and leveraging example prompts to guide the LLM's generation process. This area is significant because it allows for adapting LLMs to specific tasks with minimal computational cost, unlocking broader applications in diverse fields such as robotics, code generation, and machine translation.