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 …

Large language models do not read text exactly like humans. Before an LLM can process a sentence, the text is broken into smaller units called tokens.

This process is known as tokenization. It is one of the first steps that allows an LLM to convert human language into a format …

## 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 …

## Introduction

Large Language Models (LLMs) have changed the way applications interact with artificial intelligence. From chatbots and coding assistants to document analysis and AI agents, much of their performance depends on how information is provided to the model.

This has led to two important approaches in AI application development: Prompt …

Introduction

Modern AI models like GPT-4V, Claude, and Gemini can look at an image and describe it, answer questions about it, or reason about what's happening in it — all in natural language. These are called Vision-Language Models (VLMs), and they represent one of the most exciting frontiers in AI: teaching …

Introduction

Large Language Models (LLMs) like GPT, Claude, and Gemini can write essays, answer questions, and even generate code. But how do they actually produce text? The process is surprisingly simple at its core, even though the models themselves are massive and complex. This post breaks it down in plain …