The Complete GenAI Development Lifecycle (in Databricks)

Building compound AI systems isn’t just about writing code. It’s an end-to-end process from understanding business problems through production monitoring. The development lifecycle for GenAI applications follows a structured path — with a critical distinction between development and production phases.

The Development Lifecycle

The complete journey from problem to production follows this sequence:

Business Problem → Define Success Criteria → Data Collection → Data Preprocessing → AI System (RAG / Chain) Building → AI System Evaluation → AI System Deployment → AI System Monitoring

Two Distinct Phases

System Development (use static data): The first five stages happen in development. You work with fixed datasets. Iterate rapidly. Experiment freely. The goal is finding what works before committing to production.

Deployment & Production (deal with continuously changing new data): The last three stages handle production reality. New data arrives constantly. User behavior evolves. The system must adapt while maintaining reliability.

The Feedback Loop

The lifecycle isn’t linear. It’s iterative.