LLM EngineeringPydantic output structuring for RAG agent plansHow 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.RAGPydanticStructured Output+2 moreRead Article8 min
AI Engineering in PracticeProduction env variable parsing in Python AI servicesHow to parse environment variables in Python AI services without os.environ.get footguns. Pydantic Settings, type coercion, and the validation layer.PythonProduction AIAI Agents+2 moreRead Article9 min
AI Engineering in PracticeAPI schemas: separating db models from API responsesWhy exposing DB models as API responses leaks sensitive fields. The Pydantic separation pattern, the 3 schema layers, and the safe FastAPI response.AI AgentsAPI DevelopmentPydantic+2 moreRead Article10 min
AI Engineering in PracticeSQLModel for agentic AI: beyond JSON blob storageHow SQLModel gives agentic AI systems type-safe structured storage. The end of JSON blob columns, the Pydantic bridge, and the 50-line pattern.AI AgentsPythonPydantic+2 moreRead Article10 min
LLM EngineeringJSON output parsing for RAG: grounding with PydanticHow JSON output parsing grounds RAG answers in retrieved context. The Pydantic schema, the prompt, and the validation that kills hallucinations.RAGStructured OutputPydantic+2 moreRead Article11 min
AI EngineeringGiving your LLM hands: a deep dive on tool callingHow 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 funcAI AgentsTool CallingFunction Calling+2 moreRead Article8 min
AI EngineeringStructured output: making LLMs application-readyLearn how to get reliable, structured outputs from LLMs using JSON mode and Pydantic tool calling. Transform LLMs from text generators into application-...LLMPydanticTool CallingRead Article8 min