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Every prompt change should be tested against an eval suite before deployment. Create a set of 50-100 representative input/output pairs and run automated scoring after each prompt iteration. Without evals, you are optimizing blindly.

Test prompts with different models to find the best balance of cost and performance.

Never blindly trust code generated by an LLM. Always thoroughly test it for correctness, security vulnerabilities, and performance issues before using it in production.

In a production environment, A/B test different prompt variations to empirically determine which ones yield the best results for your key metrics.

Generate realistic mock data for testing applications, including user profiles, transaction records, and sample content that matches your schema.