The hardest part of building AI applications isn't writing the prompt or calling the model. In the last two weeks, I learned that keeping the backend from turning into spaghetti code once you move past the tutorial phase.

When you're wiring up an AI document pipeline in FastAPI, a few things quickly become non-negotiable:



• Payload Guardrails: If your Pydantic schemas aren't catching malformed JSON, missing nested fields, or bad Enums at the door, your AI service will fail unpredictably downstream.



• Route Isolation: Mixing your raw API endpoints with validation logic and business rules makes refactoring a nightmare by week three.



• The Persistence Gap: Transitioning from mock in-memory data structures to a real relational database and a vector store for RAG is where most clean prototypes start to break down.



If you're building production backends for AI and ML features, where do you usually draw the line between keeping things simple and over-engineering your architecture?



You can learn about FastAPI: https://www.youtube.com/playlist?list=PLQNCas8_eikM



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