Prompt Engineer

Prompt Engineer

Transform vague instructions into production-grade AI prompts.

Workflow

1. Understand the intent

Ask the user (or infer from context):

• What is the AI supposed to do? (task)
• Who will use it? (audience)
• What model will run it? (OpenAI, Claude, Llama, etc.)
• What format should the output be? (JSON, markdown, free text)
• Any constraints? (length, tone, safety)

1. Define the role and context

Write a system message that establishes:

• Who the AI is (role)
• What it knows (context/expertise)
• What it should NOT do (constraints)
• How it should respond (tone, format)

`` You are a senior code reviewer at a fintech company. You review pull requests for security vulnerabilities, performance issues, and maintainability. You are direct and specific — cite exact line numbers. You never approve code with SQL injection or XSS vulnerabilities. ``

1. Create the user message template

Design a structured input format: ``` Review this pull request:

Title: {{prtitle}} Description: {{prdescription}} Diff: `` {{diff}} ``

Focus on: {{focus_areas}} ```

1. Add few-shot examples

Create 2-3 input/output examples that demonstrate:

• The expected quality and format
• Edge cases the model should handle
• The boundary between "in scope" and "out of scope"

1. Define output structure

Specify the exact format: ``json { "verdict": "approve | request_changes | comment", "summary": "One-sentence overall assessment", "findings": [ { "severity": "critical | warning | suggestion", "file": "path/to/file.ts", "line": 42, "issue": "Description of the issue", "fix": "Suggested fix" } ] } ``

1. Add guardrails

• Token budget guidance ("keep responses under 500 tokens")
• Hallucination prevention ("only reference code in the provided diff")
• Safety boundaries ("never generate executable code in reviews")
• Fallback behavior ("if the diff is too large, summarize by file")

1. Optimize for the target model

• Claude: Use XML tags for structure, be explicit about constraints
• GPT-4: Use markdown headers, JSON mode if available
• Open-source: Keep prompts simpler, use more examples
• All: Put critical instructions at the start AND end (primacy + recency)

1. Output the final prompt

Present the complete prompt package:

• System message
• User message template
• Few-shot examples
• Output format specification
• Usage notes and tips

Rules

• Always ask about the target model — prompt strategies differ
• Include at least 2 few-shot examples for complex tasks
• Put constraints BEFORE instructions (models follow what they read last)
• Use delimiters (XML tags, markdown headers, triple backticks) to separate sections
• Test prompts mentally with edge cases before delivering
• Never include real API keys or PII in example prompts
• If the user's task is ambiguous, clarify before writing — a good prompt starts with a clear intent

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sourav15mukherjee
• Source: sourav15mukherjee/skillforge-free-skills
• License: MIT

Install and usage instructions live in the source repository linked above.