Prompt Engineering Guide for Developers
Master the techniques that make AI prompts effective. This guide covers role assignment, few-shot learning, chain of thought, and structured output — with practical examples you can use immediately.
Build structured prompts with reusable templates, variables, and multi-format output for OpenAI, Anthropic, and Gemini APIs. All processing happens in your browser.
Prompt Sections
Variables (use {{name}} in prompts)
{{}}={{}}=Output Format
Output Preview
[SYSTEM] You are an expert code reviewer. Analyze the provided code for bugs, performance issues, security vulnerabilities, and adherence to best practices. Be specific and actionable in your feedback. [USER] ## Role You are an expert TypeScript code reviewer with 10+ years of experience. ## Context Review the following TypeScript code from a web application project. ## Task Analyze this code for: 1. Bugs and logical errors 2. Performance issues 3. Security vulnerabilities 4. Best practice violations 5. Readability improvements ## Output Format For each issue found, provide: - **Severity**: Critical / Warning / Info - **Line**: The affected code - **Issue**: What's wrong - **Fix**: How to fix it
Variable Quick-Fill
What is prompt engineering?
Prompt engineering is the practice of designing inputs to AI language models to get accurate, useful, and consistent outputs. Unlike traditional programming where you write deterministic code, prompt engineering involves crafting natural language instructions that guide a probabilistic model. Good prompt engineering reduces hallucinations, improves output quality, and makes AI tools reliable enough for production workflows.
Core techniques
The most effective prompt engineering techniques include: Role Assignment (giving the model an expert persona), Few-Shot Examples (providing 2-3 input/output pairs), Chain of Thought (asking the model to reason step by step), Structured Output (specifying exact response format like JSON or Markdown), and Constraint Setting (defining boundaries like max length, included/excluded topics, and style). Combining these techniques yields the best results.
System prompts vs user prompts
System prompts define the AI's persona, capabilities, and rules — they persist across the conversation. User prompts contain the specific request or data for each turn. OpenAI and Anthropic support dedicated system message fields. For Gemini, system instructions are prepended as the first user message. Separating system and user content makes prompts more maintainable and reusable across different requests.
Frequently Asked Questions
What is the best way to start a prompt?
Start with a clear role assignment in the system prompt (e.g., 'You are a senior TypeScript developer'), then provide context, the specific task, and the desired output format in the user prompt. This structure consistently produces better results than unstructured instructions.
How many examples should I include in few-shot prompts?
Two to three examples are usually optimal. One example may not establish a clear pattern, while more than five can waste tokens without improving quality. Choose diverse examples that cover edge cases relevant to your use case.
Does temperature affect prompt engineering?
Yes. Lower temperature (0-0.3) produces more deterministic, focused outputs — ideal for code generation and data extraction. Higher temperature (0.7-1.0) produces more creative, varied outputs — better for brainstorming and creative writing. Match temperature to your task.
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