Five structured output patterns in five days. Which one changed how you think about AI output?
result_type. The moment I realised the API enforces the shape — not just the prompt — I stopped thinking of AI output as text to parse and started thinking of it as typed data to use directly.
That shift is Week 2's key insight. AI output is data. The LLM fills your schema. Your job is designing the schema, not parsing the string. What still trips you up?
When to use Literal vs a Pydantic model with a constrained str field. And remembering to call .model_dump() — not .dict() or .to_dict().
.model_dump() is the Pydantic v2 method — .dict() is deprecated. That's a common source of bugs. Six questions — five from this week, one review from Week 1.
Ready. Week 3's batch operations are the part I've been waiting for — running these extractions over 200 abstracts at once.
Six questions first.
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Five structured output patterns in five days. Which one changed how you think about AI output?
result_type. The moment I realised the API enforces the shape — not just the prompt — I stopped thinking of AI output as text to parse and started thinking of it as typed data to use directly.
That shift is Week 2's key insight. AI output is data. The LLM fills your schema. Your job is designing the schema, not parsing the string. What still trips you up?
When to use Literal vs a Pydantic model with a constrained str field. And remembering to call .model_dump() — not .dict() or .to_dict().
.model_dump() is the Pydantic v2 method — .dict() is deprecated. That's a common source of bugs. Six questions — five from this week, one review from Week 1.
Ready. Week 3's batch operations are the part I've been waiting for — running these extractions over 200 abstracts at once.
Six questions first.