Most developers learn FastAPI through the basics—routes, request bodies, response models, and dependency injection. That's enough to build APIs, but FastAPI has several powerful features that rarely appear in beginner tutorials.

These hidden gems can help you write cleaner code, improve performance, reduce boilerplate, and build production-ready applications more …

FastAPI looks simple on the surface. You define an endpoint, send a request, and get a response. But behind that simplicity is a surprisingly sophisticated pipeline that handles routing, validation, dependency injection, serialization, and more.

Understanding how a request travels through FastAPI helps you write better APIs, debug issues faster …

Introduction

In this session, I built an AI-powered Notes application by integrating a local Large Language Model with FastAPI and MongoDB.

This project goes beyond basic APIs by combining model inference, backend logic, and persistent storage — similar to how real-world AI systems operate.

The goal was to create a system …

Introduction

Day 6 of my GenAI Learning Challenge.

Today, I integrated a local LLM with FastAPI to build a usable AI backend. Instead of running the model separately, user input is sent through an API and processed by llama.cpp.

This is a key step toward building real AI applications …

Introduction

In Generative AI systems, models do not work in isolation. They rely on APIs to receive input, process data, and return responses.

To understand how backend systems work in AI applications, I built my first API using FastAPI.

In this session, I focused on:

• Creating API endpoints
• Handling data …