prompt-engineering

Prompt Engineering

Core Philosophy

A great prompt is not a magic spell — it's a specification. The more precisely you define the goal, context, constraints, and output format, the more reliably the AI performs. Think of prompting like writing a function signature: the model is the implementation, you are the interface designer.

The Golden Rule: If the output isn't what you wanted, the problem is almost always in the prompt — not the model.

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Prompt Anatomy (4-Part Structure)

Every effective prompt has four components:

ROLE:    Who the AI should be (expertise, perspective, voice)
GOAL:    What you want done (specific, unambiguous task)
CONTEXT: Relevant background, data, constraints, examples
OUTPUT:  Exact format, length, structure of the response

Example

ROLE: You are a senior TypeScript developer with expertise in React and Next.js.
GOAL: Review this component for performance issues and suggest specific improvements.
CONTEXT: This is a client component in Next.js 15 App Router. 
         Performance is critical — this renders on every keystroke.
         [paste code here]
OUTPUT: List issues as BLOCKER / WARNING / SUGGESTION with specific fix for each.

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Core Techniques

Zero-Shot

Ask directly with no examples. Works for well-understood tasks.

Write a conventional commit message for this diff: [diff]

Few-Shot

Provide 2–3 examples before the real task. Best for format-sensitive or pattern-matching tasks.

Examples:
Input: Added login form → Output: feat(auth): add login form with email/password
Input: Fixed null crash → Output: fix(api): handle null response from user endpoint

Now write a commit message for: [diff]

Chain-of-Thought (CoT)

Force step-by-step reasoning. Add "Think step by step" or "Reason through this before answering."

Is this API design RESTful? Think step by step before answering.

Role / Persona Assignment

Set expertise level and perspective upfront.

You are a security engineer specializing in OWASP Top 10.
Review this authentication code for vulnerabilities.