ai-product-patterns

AI-native product specifications are defined through runnable test cases called evals. This approach replaces traditional requirements documents with executable success criteria that validate model behavior and track product quality.

Design AI UX patterns by assuming model capabilities will rapidly improve over time. Implement adaptive interfaces using streaming and progressive disclosure to handle changing AI outputs without breaking the user experience.

A hybrid approach in AI product strategy combines AI model strengths with traditional code to create robust solutions. This method determines which product components require AI inference versus deterministic logic for optimal reliability.

Yes, you can use evaluations as product specs for AI search features by defining success criteria via evals. This allows you to measure retrieval accuracy, plan for future model enhancements, and validate performance systematically.

You should avoid a fully AI-driven approach when deterministic outcomes are critical. Implementing a hybrid approach that combines AI with traditional code ensures necessary reliability and prevents unpredictable model behavior in core workflows.