Prompt Engineering Guide: 10 Techniques That Actually Work
Why Prompting Matters
The difference between a mediocre AI response and an exceptional one is almost always the prompt. The same model that produces generic fluff from a vague prompt will produce expert-level output from a well-structured one.
1. Be Specific About Format
Bad: “Write about Python decorators” Good: “Write a technical tutorial on Python decorators. Include 3 code examples progressing from basic to advanced. Each example should have inline comments explaining what happens at each step.”
Specificity about format (tutorial, list, comparison), length, and structure dramatically improves output quality.
2. Provide Context and Role
“You are a senior backend engineer reviewing code for a production deployment. Analyze this function for performance issues, security vulnerabilities, and edge cases.”
Giving the AI a role and context frames its response with the appropriate expertise level and perspective.
For more on this topic, see our guide on How to Set Up an AI Pair Programming Workflow That Actually Works.
3. Chain of Thought (CoT)
Add “Think through this step by step” or “Let’s work through this systematically” to complex reasoning tasks. Research from Google and OpenAI shows CoT prompting significantly improves accuracy on math, logic, and multi-step reasoning problems.
4. Few-Shot Examples
Provide 2-3 examples of the input/output format you want before giving your actual request. The model pattern-matches to your examples.
Input: "The API returned a 429 error"
Classification: Rate Limiting
Severity: Medium
Input: "Database connection pool exhausted"
Classification: Infrastructure
Severity: Critical
Input: "User reported slow page loads on mobile"
Classification: [your turn]5. Constrain the Output
“Respond in JSON format with keys: summary, severity, recommendation” “Keep your response under 200 words” “List exactly 5 items, no more, no less”
Constraints prevent rambling and ensure the output is immediately usable.
6. Negative Prompting
Tell the model what NOT to do: “Do not include generic advice. Do not use placeholder values. Do not add disclaimers.”
7. Iterative Refinement
Start broad, then refine. First prompt generates the skeleton. Second prompt improves specific sections. Third prompt polishes. This produces better results than trying to get perfection in one shot.
8. Temperature Control (API)
Temperature 0.0-0.3 for factual, deterministic tasks (code generation, data extraction). Temperature 0.7-1.0 for creative tasks (brainstorming, writing). Most APIs default to 0.7.
9. System Prompts for Consistency
When using APIs, put persistent instructions in the system prompt and variable content in the user prompt. This maintains consistent behavior across conversations.
10. Ask the Model to Self-Critique
“Now review your response. Identify any errors, missing edge cases, or improvements.” Models catch their own mistakes surprisingly well when explicitly asked.