Python apps with LLM building blocks

"In this episode, I'm talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We'll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture. Watch the live stream version Episode Deep Dive Guest Introduction and Background"

"Decorators and functions: LLM integration often uses Python decorators to wrap functions with caching, validation, or API calls. Understanding how functions work and how decorators modify their behavior is essential. Type hints and Pydantic: Modern LLM work relies heavily on type annotations to define structured outputs. Familiarity with Python's type hint system (strings, integers, lists, optional types) will help you understand how to constrain LLM responses. Async programming basics: While not deeply covered, many LLM APIs benefit from async/await patterns for better performance w"

"Vincent Warmerdam is a data scientist, educator, and developer advocate currently working at Marimo, a modern notebook environment for Python. He's best known for his educational work through CalmCode.io, where he provides free Python tutorials, and his YouTube channels covering both Python programming and ergonomic keyboards (which has grown to over 5,000 subscribers). Vincent previously worked at Explosion (makers of spaCy) and has extensive experience in machine learning and natural language processing. He's a strong advocate for pragmatic, boring technology that just works, and has a passion for building tools that make developers more productive."

LLMs should be treated as standard APIs within Python applications, with clear boundaries and small, focused endpoints. Wrapping LLM calls with lightweight functions or decorators enables caching, validation, and centralized monitoring. Type hints and Pydantic models help constrain and parse structured outputs from LLM responses. Async/await patterns improve throughput for many LLM API integrations. Inspecting and logging responses aids in debugging, metric collection, and determining where LLMs add real value versus simpler deterministic code. Architectures should include fallbacks, rate-limiting, and cost-awareness to control latency and spending. Small well-tested endpoints make LLM usage predictable and maintainable.

Read at Talkpython