Introduction

Large Language Models (LLMs) can generate human-like responses, but they may sometimes provide inaccurate information or struggle with questions that require understanding relationships across multiple documents. Retrieval-Augmented Generation (RAG) helps address these limitations by retrieving relevant information before generating an answer.

However, traditional RAG often focuses on retrieving text …

Introduction

Generative AI applications use language models to generate text, answer questions, and automate tasks. FastAPI provides a Python-based framework for connecting these models to web applications through APIs.

By combining FastAPI with an LLM, developers can build AI-powered chatbots, content generators, document assistants, and intelligent automation tools.

What Is …

Introduction

Generative AI has changed how machines create text, images, audio, videos, and code. Behind these capabilities are powerful machine learning architectures called neural networks.

Neural networks help AI systems learn patterns from large amounts of data. Instead of following only predefined rules, they identify relationships within the training data …

Introduction

Large Language Models (LLMs) can answer questions, generate code, summarize information, and explain complex topics.

But answering a question is different from completing a task.

For example, an LLM can explain how to check an order status, but an AI agent with access to an order system can actually …

Introduction

Most people have used a customer service chatbot that didn't help.

It answered a question no one asked. It couldn't see the order that was already on screen. It looped back to "How else can I assist you today?" three times in a row before handing off to a …

Introduction

AI agents are different from traditional chatbots because they can do more than generate text. An agent can interact with external tools such as APIs, databases, search engines, calculators, code interpreters, file systems, and business applications.

But this creates an important question:

How does an AI agent decide which …

Introduction

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Generative AI has transformed the way applications understand language, generate content, analyze information, and interact with users. However, many GenAI applications depend on cloud servers for model inference, which can introduce network latency, privacy concerns, and connectivity requirements.

Edge AI addresses this limitation by moving AI processing closer to …

## Introduction

Chatbots and AI agents are both becoming common in modern applications, but they are not the same.

A chatbot is primarily designed to communicate with users and provide responses, while an AI agent is designed to understand a goal, make decisions, use tools, and take actions to accomplish that …

## Introduction

AI agents are moving beyond simply generating text. Modern AI agents can interact with external systems, retrieve information, call APIs, query databases, execute functions, send messages, and perform actions on behalf of users.

This capability is commonly enabled through tool calling or function calling.

For example, an AI travel …

## Introduction

When interacting with a Generative AI system, we often focus on the question we ask and the answer we receive. However, there is another important factor working behind the scenes: the context window.

The context window determines how much information an AI model can consider at one time while …