Prompt Engineering

Prompt engineering is the practice of giving clear instructions to an AI model so it produces useful, reliable output.

Simple idea: better instruction, better answer.

Why It Matters

Prompts control how the model behaves:

• What role it should take.
• What task it should complete.
• What context it should use.
• What format it should return.
• What it should avoid.

Good Prompt Structure

Use this simple format:

Role: You are a privacy compliance assistant.
Task: Summarize this policy change.
Context: Use the notes below.
Rules: Be concise. Do not invent laws.
Output: Return bullet points with risks and next actions.

Prompt Checklist

• Define the role.
• Give the exact task.
• Add relevant context.
• Specify the output format.
• Add constraints.
• Ask for uncertainty when needed.
• Test with real examples.

Common Patterns

Pattern	Use
Summarize	Turn long content into clear notes
Extract	Pull structured fields from text
Classify	Put items into categories
Rewrite	Change tone, length, or clarity
Compare	Evaluate options
Plan	Create steps and dependencies
Critique	Find gaps, risks, or missing details

Bad Prompt

Analyze this.

Better Prompt

Analyze this customer complaint for a SaaS privacy product.
Return:
1. Core issue
2. Likely root cause
3. Risk level
4. Suggested response
5. Product improvement idea

CookieYes Ideas

• Prompt templates for GDPR policy summaries.
• Prompt templates for cookie classification.
• Prompt templates for customer-support replies.
• Prompt templates for AI data-leakage risk review.

Mozilor Ideas

• Prompt templates for accessibility remediation.
• Prompt templates for e-commerce agent actions.
• Prompt templates for technical documentation.
• Prompt templates for support-ticket triage.

Related

• LLM
• RAG
• Agents