Prompt Engineering
読み方：Prompt Engineering
The practice of designing and optimizing prompts (inputs) to large language models to achieve desired outputs. Effective prompting can dramatically improve LLM output quality for the same model. Key techniques include few-shot examples, chain-of-thought reasoning, and system prompts.
What is Prompt Engineering
Prompt engineering is the skill of communicating with LLMs effectively. The same model produces dramatically different quality outputs depending on how questions and instructions are framed.
Core Techniques
### Zero-Shot Prompting
Direct instructions with no examples. Works for straightforward tasks.
### Few-Shot Prompting
Provide 2–5 input/output examples before the actual task. Significantly improves output format consistency and quality.
### Chain-of-Thought (CoT)
Add "Let's think step by step" or break complex problems into explicit reasoning steps. Dramatically improves performance on math, logic, and multi-step reasoning tasks.
### System Prompts
Define the AI's role, constraints, and output style upfront. "You are an expert copywriter writing for a B2B SaaS audience. Write in a direct, specific, no-jargon style."
Elements of an Effective Prompt
1. Clear instruction: Exactly what output is wanted
2. Context: Who is the audience, what is the purpose
3. Constraints: Length, format, tone, things to avoid
4. Examples: Sample inputs and outputs (for few-shot)
Why It Matters
Prompt quality determines AI output quality. Teams that invest in prompt design and documentation get dramatically better results from the same models as teams that don't.