LLM Prompt Engineering: 10 Best Practices for Better AI Outputs (2026)

Category: Learning Difficulty: Intermediate Updated: 2026-05-26

Learn prompt engineering best practices for ChatGPT, Claude, Gemini, and other LLMs. Templates, techniques, and patterns for consistent high-quality outputs.

The Fundamental Principle

LLMs follow patterns and examples better than instructions. Show, don't just tell. Every prompt should include: Role + Context + Task + Format + Examples.

10 Best Practices

  1. Set a role: "You are an expert React developer with 10 years experience"
  2. Be specific: Replace "write code" with "write a TypeScript function that validates email format using regex"
  3. Provide examples: Show input → expected output pairs
  4. Specify format: Output as JSON, markdown, table, or code block
  5. Chain complex tasks: Break into steps, use each output as next input
  6. Add constraints: "No external dependencies", "Must pass strict TypeScript checks"
  7. Use delimiters: Wrap code/data in triple backticks or XML tags
  8. Ask for reasoning: "Explain your approach before giving the answer"
  9. Iterate: Refine outputs by providing specific feedback
  10. Few-shot prompting: Provide 2-3 examples for complex tasks