BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

755Why Structured Output Matters
756JSON Mode Basics
757Enabling JSON Mode in API Calls
758Schema-Free JSON Generation
759Schema Definition in Prompts
760Function Calling for Structured Output
761Defining Function Schemas
762Nested Objects and Arrays
763Error Handling for Malformed Output
764Choosing Between JSON Mode and Functions
765Pydantic Basics for LLM Output
766Defining Field Types and Constraints
767Nested Models and Complex Schemas
768Optional Fields and Default Values
769Enums and Literal Types
770Custom Validators and Business Logic
771Parsing LLM JSON into Pydantic Models
772Schema Generation for Prompts
773Handling Validation Errors
774Model Configuration and Serialization
775Union Types and Discriminated Unions
776Integration with LLM Frameworks
777What is Grammar-Based Generation
778Context-Free Grammars (CFG) Basics
779Logit Biasing and Token Masking
780Guidance Library for Constrained Generation
781Outlines Library for Structured Output
782GBNF (GGML BNF) for llama.cpp
783Performance Trade-offs of Grammar Constraints
784Combining Grammars with Few-Shot Prompting
785Debugging Grammar Constraint Failures
786When to Use Grammar-Based vs JSON Mode
787Nested Objects and Array Handling
788Union Types and Discriminated Unions
789Optional Fields and Default Values
790Schema Evolution and Versioning
791Handling Schema Validation Failures
792Large Schema Prompting Strategies
793Cross-Field Validation and Constraints
794Schema Documentation for LLMs

755Why Structured Output Matters
756JSON Mode Basics
757Enabling JSON Mode in API Calls
758Schema-Free JSON Generation
759Schema Definition in Prompts
760Function Calling for Structured Output
761Defining Function Schemas
762Nested Objects and Arrays
763Error Handling for Malformed Output
764Choosing Between JSON Mode and Functions
765Pydantic Basics for LLM Output
766Defining Field Types and Constraints
767Nested Models and Complex Schemas
768Optional Fields and Default Values
769Enums and Literal Types
770Custom Validators and Business Logic
771Parsing LLM JSON into Pydantic Models
772Schema Generation for Prompts
773Handling Validation Errors
774Model Configuration and Serialization
775Union Types and Discriminated Unions
776Integration with LLM Frameworks
777What is Grammar-Based Generation
778Context-Free Grammars (CFG) Basics
779Logit Biasing and Token Masking
780Guidance Library for Constrained Generation
781Outlines Library for Structured Output
782GBNF (GGML BNF) for llama.cpp
783Performance Trade-offs of Grammar Constraints
784Combining Grammars with Few-Shot Prompting
785Debugging Grammar Constraint Failures
786When to Use Grammar-Based vs JSON Mode
787Nested Objects and Array Handling
788Union Types and Discriminated Unions
789Optional Fields and Default Values
790Schema Evolution and Versioning
791Handling Schema Validation Failures
792Large Schema Prompting Strategies
793Cross-Field Validation and Constraints
794Schema Documentation for LLMs

← AI Engineering

Lesson 765 of 1,886·19. Structured Output and ValidationPro lesson

Pydantic Basics for LLM Output

Learn how Pydantic models define type-safe schemas that LLMs can generate and Python can validate automatically.

This lesson is for subscribers

You've completed the free preview. Subscribe to unlock every lesson in every course.

See pricing Back to course