This lecture covers FastAPI's use of Pydantic for data validation in POST requests. Pydantic defines schemas, enforcing data structure and type. It automatically validates incoming data, throwing errors for missing or incorrectly typed fields (e.g., string vs. integer). Optional fields and default values are demonstrated, along with converting Pydantic models to dictionaries for responses. This segment highlights the importance of defining a clear schema for API data to ensure data integrity and prevent the frontend from sending arbitrary data. The speaker emphasizes the need for a contract between the frontend and backend, specifying the expected data structure and triggering errors if the data doesn't conform to the schema. This establishes best practices for robust API design and error handling. This segment introduces Pydantic, a Python library used to define data schemas. The speaker clarifies that Pydantic is independent of FastAPI, emphasizing its broader applicability in Python applications. This segment provides a foundational understanding of Pydantic's role in data validation and schema enforcement within the context of API development. This segment demonstrates Pydantic's validation capabilities by showcasing how it handles missing fields and incorrect data types. The speaker illustrates the creation of optional fields using `Optional` from the `typing` library and demonstrates how Pydantic automatically throws errors when required fields are missing or data types don't match the defined schema. This practical demonstration reinforces the importance of schema validation for data integrity.