“pydantic”
Injection-Resistant Tool Schemas: The Case for Strict Structured Inputs
Free-form string parameters are the largest prompt-injection surface in an agent. Tighten tool inputs with strict Pydantic schemas, enums, validators as policy boundaries, and reject-don't-sanitize semantics — and know what schemas cannot fix.
Contract Testing Your Agents: Asserting Behavior Before Deployment
Stop asserting on prose. Define the inputs, tool schemas, output shapes, and side effects an agent must honor, then test those contracts deterministically in CI.
Validating Tool Outputs with Pydantic Contracts
Learn how to enforce rigorous Pydantic schema validation on external tool outputs in AI agent pipelines, preventing downstream corruption and silent failures.
Designing Tool Schemas That Models Won't Misuse
Design tool schemas that prevent LLM misuse. Use clear descriptions, strict type validation, and defensive patterns to keep agents on track.
Structured Outputs for Agents: JSON Schema Validation with Pydantic
Ensure reliable agent outputs with JSON Schema validation using Pydantic. Learn to define typed tool outputs, validate LLM responses, and build production-grade structured output pipelines.
Structured Outputs: JSON Schema Validation for LLMs
Ask an LLM for JSON and you will get broken JSON. Learn to constrain generation with Gemini response_schema and Pydantic, then verify the output as an explicit, catchable step.
Why Pydantic is the Unsung Hero of Modern LLM Application Development
Structured data is the bridge between chaotic AI outputs and reliable applications. Discover why Pydantic is essential for building robust LLM-powered tools.
Generating Embeddings with Pydantic AI
A comprehensive guide on how to generate embeddings using Pydantic AI across multiple providers like OpenAI, Google, Cohere, Bedrock, and VoyageAI.