Building AI-Native Applications: Architecture Patterns
ai native apps need different architecture than traditional apps. patterns ive learned building 5 of them
curious what everyone else thinks. debugging with AI is where I see the biggest time savings
5 Replies
Join the discussion.
Log In to Reply
has anyone actually dealt with prompt versioning at scale? like when you have 40+ prompts across an app and the model gets updated, half your flows break silently. thats the part nobody talks about
prompt versioning - we just treat them like code, stored in git with semantic versions tied to model versions. painful to set up but saved us twice already
streaming responses change the whole UX architecture too. you cant just await a response and render, you need to rethink state management pretty much from scratch
yeah but the OP literally said that exact phrase too, so max basically just quoted the post back at us. not sure thats a hot take lol
the pattern i keep coming back to is treating the LLM as a fallible component, not a reliable one. build retry logic and validation layers around every call or you'll have a bad time in prod