Prompt Engineering

Overview

Prompt engineering improves LLM output quality through input design rather than retraining. Techniques include role assignment ('You are an expert in...'), output-format specification, Chain-of-Thought (CoT) reasoning cues, and few-shot examples. Because optimal prompts shift with model updates, ongoing testing is necessary.

Core techniques

The baseline structure is: a System Prompt defining role, constraints, and format; few-shot examples showing expected I/O patterns; and CoT cues for complex reasoning tasks. See OpenClaw Prompt Engineering Tips for practical guidance.