CQRS
Command Query Responsibility Segregation is an architectural pattern that separates read and write operations into distinct models. Write operations use command models optimized for validation and business logic, while read operations use query models optimized for data retrieval.
CQRS recognizes that read and write patterns often have fundamentally different requirements. Writes need to enforce business rules, validate data, and maintain consistency. Reads need to be fast, support complex queries, and may benefit from denormalized data structures. By separating these concerns, each side can be optimized independently, scaled separately, and evolved without compromising the other.
For AI product teams, CQRS is particularly relevant because AI features often have dramatically different read and write characteristics. Writing new user interactions, content, or feedback to the system requires transactional consistency and validation. Reading data for model inference, generating recommendations, or powering search requires fast, complex queries across denormalized views. Growth teams benefit from CQRS because experiment analysis queries can be served from read-optimized projections without impacting the write path that records user interactions. The pattern naturally supports event-driven AI pipelines: commands generate events, events update both the write store and read-optimized projections, and AI services query the projections for real-time inference.
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