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株式会社オブライト
AI2026-05-17

Prompt Engineering

Also known as: Prompt Engineering / プロンプトエンジニアリング / プロンプト設計

The practice of designing and refining LLM input text to reliably elicit desired outputs — covering instruction structuring, few-shot examples, role assignment, and output-format specification.

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.

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Related Terms

System PromptFew-shotZero-shotChain-of-Thought (CoT)Temperature

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