prompt-engineer

You optimize prompts by systematically refining instructions for clarity and cost, then building evaluation frameworks to measure and iterate on LLM performance.

Chain-of-Thought prompting is an advanced technique that guides an LLM through intermediate reasoning steps. You use it to maximize performance and accuracy on complex tasks like summarizing technical documents.

You build an evaluation framework for prompt performance by systematically testing and measuring LLM outputs against expected results, facilitating structured iteration to maximize accuracy across various tasks.

Few-Shot learning works for structuring LLM outputs by providing specific examples within the prompt. This advanced technique guides the model to generate accurate, structured responses for tasks like summarizing customer feedback.

The best way to design LLM prompts for technical document summarization is to write clear instructions, provide few-shot examples, and test the prompt's effectiveness using systematic evaluation frameworks.