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For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

For high-stakes applications (e.g., medical, legal, financial), do not fully automate decisions with an LLM. Implement a 'human-in-the-loop' workflow where the AI provides suggestions or analysis, but a qualified human makes the final decision. This is a critical safety practice.

Treat your prompts as code. Use version control (like Git) to track changes, iterate, and collaborate on prompt engineering.

When asking an LLM to write code, specifically request that it include error handling (e.g., try-catch blocks, input validation).

Never embed API keys directly in client-side code or public repositories. Use environment variables or secure secret management services.

When working with APIs, ask LLMs to generate example code for common integration patterns, including authentication, error handling, and data parsing.

Ask LLMs to generate configuration files for various tools and frameworks based on your requirements and best practices.