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, most models are accessed through APIs. However, running models locally is becoming increasingly important for privacy, cost efficiency, and offline usage.

To understand how local inference works, I ran a Large Language Model (LLM) on my system using llama.cpp.

In this session, I focused …

Introduction

In AI systems, data should not be lost after execution. Persistent storage helps store data permanently.

Use

• Store user inputs
• Save AI responses
• Maintain history

On Day 4, I connected my Python application to MongoDB to build a persistent backend system.

Understanding Persistent Storage

Persistent storage means saving data …

Introduction

In real-world AI systems, models alone are not enough. They need memory, structured outputs, and backend logic to work effectively.

To understand this, I built a simple end-to-end AI pipeline using a local model (llama.cpp) and MongoDB.

In this session, I focused on:

• Running a local AI model …

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 …

Introduction

In Generative AI (GenAI), building models is only one part of the process. The real foundation lies in how we handle and structure data before it reaches the model.

To get started, I focused on three essential Python concepts:

• Variables
• Lists
• Dictionaries

These form the backbone of how data …