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

Be aware of API rate limits (requests per minute/day). Design your application to handle these gracefully, perhaps with retries and exponential backoff.

Implement fallback strategies in your application for when the LLM fails, returns an error, or provides an unsatisfactory response. Don't let it be a single point of failure.

Challenge: Design a prompt that produces consistent, high-quality results across different LLM models (GPT, Claude, Gemini). Test and refine until you achieve similar outputs.