The original title is "Building Production AI Systems: Designing Around Capabilities, Not Models"

Short summary

A practical guide to building production AI systems that survive model churn by abstracting around capabilities, not specific models. Key principles include versioning prompts like code, using feature flags for model rollouts, building your own evaluation benchmarks, and routing all LLM calls through interfaces so provider swaps become configuration changes. The core message: treat AI integration with the same engineering discipline as traditional software.

• •Build around capabilities, not specific models — use interfaces so provider swaps are config changes
• •Version prompts like code: review, document, and roll back changes
• •Use feature flags and custom eval suites instead of trusting public leaderboards

Generated with AI, which can make mistakes.