1. Introduction Machine learning (ML) models, especially those involved in natural language processing (NLP) and large language models (LLMs) such as GPT, BERT, or Gemini, rely on three fundamental concepts: parameters, tokens, and context. These three ideas describe how: To understand how modern AI systems operate — how they read, remember, and respond intelligently —…

In this tutorial, we continue our exploration of text splitting techniques in LangChain, focusing on one of the most fundamental operations in any Retrieval-Augmented Generation (RAG) pipeline — preparing text data for large language models. In the previous tutorial, we discussed the Recursive Character Text Splitter, learned how to use it in code, and explored…

In this tutorial, we continue our journey into LangChain, a powerful framework that connects large language models (LLMs) with external data sources and tools. In the previous part, we explored data ingestion techniques using different document loaders to read content from PDFs, text files, web pages, and Wikipedia. Now, we move to the next critical…

In this tutorial, we will explore how data ingestion works in LangChain — the very first step in building any Generative AI pipeline. LangChain provides a structured way to load, transform, and store data so that it can later be used for retrieval-based question answering, semantic search, or chatbot applications powered by large language models…

1. What is RAG and Why It Matters Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with language model generation. It allows Large Language Models (LLMs) to generate responses that are grounded in external, up-to-date, and domain-specific information, rather than relying solely on what they learned during training. For example, imagine you have…

Introduction ChatGPT is not a single-stage model—it’s the product of a carefully designed, multi-phase training pipeline. Each phase improves upon the last to transform a raw language model into an intelligent conversational assistant. The three main stages of ChatGPT’s training are: This tutorial will describe each stage, explain why it is necessary, and highlight the…

Every neural network, whether it is a simple Feedforward Neural Network (FNN) or an advanced Recurrent Neural Network (RNN), is made up of small computational units known as neurons. Each neuron performs a simple mathematical operation:it takes some inputs, multiplies them by weights, adds a bias, and then applies an activation function to produce an…

Artificial Neural Networks (ANNs) are the foundation of modern deep learning systems. Inspired by how the human brain processes information, ANNs are capable of learning from data, identifying complex patterns, and making intelligent decisions without explicit rule-based programming. This tutorial will cover the intuition, architecture, working mechanism, a detailed example, visual representation, and advantages and…

1. Introduction Natural Language Processing (NLP) is a field of Artificial Intelligence that enables machines to understand, interpret, and generate human language.Before deep learning, NLP relied heavily on manual feature engineering, such as bag-of-words, TF-IDF, and n-grams.These methods treated text as discrete tokens and failed to capture the true meaning, context, or relationships between words.…

1. Introduction After understanding the theoretical intuition behind Word2Vec (CBOW and Skip-gram), the next logical step is to implement it in practice.In real-world NLP applications, we rarely train Word2Vec from scratch on small data; instead, we either train it on large corpora or use pre-trained embeddings. This tutorial walks through a complete implementation using Python’s…