Building LLM Applications with Python

LLM applications are not just prompts — they are systems. Data ingestion, tool interfaces, observability and UI clarity matter as much as model choice.

Python backends with FastAPI provide a clean layer for agent orchestration, while the frontend focuses on presenting insights without overwhelming users.

Local models through Ollama are useful for development and privacy-sensitive workflows. Hosted models make sense when latency and scale requirements grow.

The goal is not to showcase AI for its own sake. The interface should make complex operational data feel understandable.