AI Practitioner

Byte Pair Encoding (BPE) is one of the most important and widely adopted subword tokenization algorithms in modern Natural Language Processing (NLP), especially in training Large Language Models (LLMs) like GPT. This guide provides a deep technical dive into how BPE works, compares it with other tokenizers like WordPiece and SentencePiece, and explains its practical implementation with Python code. This article is optimized for AI engineers building real-world models and systems. 1. What is Byte Pair Encoding? BPE was originally introduced as a data compression algorithm by Gage in 1994. It replaces the most frequent pair of bytes in a sequence with a single, unused byte. In 2015, Sennrich et al. adapted BPE for NLP to address the out-of-vocabulary (OOV) problem in neural machine translation. Instead of working with full words, BPE decomposes them into subword units that can be recombined to represent rare or unseen words. 2. Why Tokenization Matters in LLMs Tokenization is th...

Understanding Retrieval-Augmented Generation (RAG): Architecture, Variants, and Best Practices Retrieval-Augmented Generation (RAG) is a hybrid approach that combines large language models (LLMs) with external knowledge retrieval systems. Instead of relying solely on the parametric knowledge embedded within LLM weights, RAG enables dynamic, non-parametric access to external sources—most commonly via vector databases—allowing LLMs to generate factually grounded and context-rich responses.  The simplest form of RAG can be seen when a user of generative AI includes specific domain knowledge—such as a URL or a PDF document—along with their prompt to get more accurate responses. In this case, the user manually attaches external references to help the AI generate answers based on specialized information. A RAG system automates this process. It stores various domain-specific documents in a database and, whenever a user asks a question, it retrieves relevant information and appends it...

How to Save and Retrieve a Vector Database using LangChain, FAISS, and Gemini Embeddings Efficient storage and retrieval of vector databases is foundational for building intelligent retrieval-augmented generation (RAG) systems using large language models (LLMs). In this guide, we’ll walk through a professional-grade Python implementation that utilizes LangChain with FAISS and Google Gemini Embeddings to store document embeddings and retrieve similar information. This setup is highly suitable for advanced machine learning (ML) and deep learning (DL) engineers who work with semantic search and retrieval pipelines. Why Vector Databases Matter in LLM Applications Traditional keyword-based search systems fall short when it comes to understanding semantic meaning. Vector databases store high-dimensional embeddings of text data, allowing for approximate nearest-neighbor (ANN) searches based on semantic similarity. These capabilities are critical in applications like: Question Ans...