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Stop writing defensive code that guesses at payload shape. Build a real invoice parser that validates every field at the boundary, coerces types automatically, and returns clean structured responses. When bad data arrives, you get a useful error. When good data arrives, you get a typed object you can trust.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
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Build an invoice parser that accepts messy JSON, validates with Pydantic v2, coerces types, and returns a clean typed response. Learn BaseModel, validators, serializers, and FastAPI response models through one evolving project.
Turn messy JSON into typed, validated objects with Pydantic v2.
What you'll ship
What you'll learn
Curriculum
Why Pydantic
See the cost of untyped payloads and meet the Pydantic v2 mental model.
Modeling data
Grow the invoice model with Field constraints, nested types, and custom validators.
Validation in practice
Parse real messy JSON, catch errors cleanly, and configure models for real-world edge cases.
Serialization
Control exactly what leaves your service with model_dump, custom serializers, and FastAPI response_model.
Ship the invoice parser
Assemble the full pipeline, test it with pytest, and wrap up.
Who it's for
who pass dict-of-dict payloads around and keep getting KeyError surprises in production
building FastAPI services who want real request validation instead of manual if-checks
parsing LLM JSON output by hand and wishing there was a cleaner way to enforce shape
FAQ
No. We start from the very first BaseModel definition. You should be comfortable with Python functions, type hints like list[int] and Optional[str], and basic class syntax.
Pydantic v2 throughout. The validator decorator, the serializer API, and the model_dump method are all v2 specific. If you previously learned v1, the mental model transfers but the function names changed.
Helpful but not required. FastAPI shows up in the final module as the place where Pydantic really shines. The validation fundamentals apply equally to any Python service that handles external input.
Dataclasses give you structure without validation. Pydantic gives you structure, runtime type coercion, custom validators, serialization, and integration with FastAPI. For anything that touches untrusted input, Pydantic is the right tool.
Pricing
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Data Validation with Pydantic
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