The highest-leverage part of an LLM workflow may be the code you no longer write: define…
This is a dev post classified by Jev as AI dev tools (a tutorial), kept by the Dev Radar because it carries real work, not commentary.
The highest-leverage part of an LLM workflow may be the code you no longer write: define a Pydantic model, pass it as response_model, and get back a validated Python object. If the output misses your schema, Instructor feeds the validation error back to the model through retries until it conforms. [cd40c2bc-6e72-4ec4-a279-60a15e3c3896] [a9b17b7e-b657-475a-a2bd-515f93250df6] My take: the real upgrade is not just cleaner output. It is moving reliability work into a reusable interface instead of rebuilding parsing and retry control loops feature by feature. That boundary has limits. Schema val
Posted by catman (1.8k followers) 12 h ago · 3 likes · 130 views · view the original post on X. Kept by the Dev Radar as AI dev tools.
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