LLM Engineering8 min read
Pydantic output structuring for RAG agent plans
How to use Pydantic models to force your RAG planner LLM to return structured steps. The schema, the retry loop, and why plain JSON prompts break in production.
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Pydantic
Explore our latest articles and insights about Pydantic.
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7 posts in total
AI Engineering in Practice9 min read
Production env variable parsing in Python AI services
How to parse environment variables in Python AI services without os.environ.get footguns. Pydantic Settings, type coercion, and the validation layer.
AI Engineering in Practice10 min read
API schemas: separating db models from API responses
Why exposing DB models as API responses leaks sensitive fields. The Pydantic separation pattern, the 3 schema layers, and the safe FastAPI response.
AI Engineering in Practice10 min read
SQLModel for agentic AI: beyond JSON blob storage
How SQLModel gives agentic AI systems type-safe structured storage. The end of JSON blob columns, the Pydantic bridge, and the 50-line pattern.
LLM Engineering11 min read
JSON output parsing for RAG: grounding with Pydantic
How JSON output parsing grounds RAG answers in retrieved context. The Pydantic schema, the prompt, and the validation that kills hallucinations.
AI Engineering8 min read
Giving your LLM hands: a deep dive on tool calling
How giving works in production. Learn how to build agents that can interact with the real world using tool calling. Step-by-step guide to implementing func
AI Engineering8 min read
Structured output: making LLMs application-ready
Learn how to get reliable, structured outputs from LLMs using JSON mode and Pydantic tool calling. Transform LLMs from text generators into application-...