AI Practitioner

In the realm of Natural Language Processing (NLP), tokenization plays a pivotal role in preparing text data for machine learning models. Traditional tokenization methods often rely on language-specific rules and pre-tokenized inputs, which can be limiting when dealing with diverse languages and scripts. Enter SentencePiece—a language-independent tokenizer and detokenizer designed to address these challenges and streamline the preprocessing pipeline for neural text processing systems. What is SentencePiece? SentencePiece is an open-source tokenizer and detokenizer developed by Google, tailored for neural-based text processing tasks such as Neural Machine Translation (NMT). Unlike conventional tokenizers that depend on whitespace and language-specific rules, SentencePiece treats the input text as a raw byte sequence, enabling it to process languages without explicit word boundaries, such as Japanese, Chinese, and Korean. This approach allows SentencePiece to train subword models di...

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...

Implementation of RoPE Rotary Position Embedding (RoPE) is a positional encoding method introduced in the 2021 RoFormer paper ( https://arxiv.org/pdf/2104.09864 ). This technique overcomes the limitations of absolute positional encoding and enhances a Transformer model's ability to capture sequence order and relative positions effectively. 1. Limitations of Traditional Positional Encoding Since Transformers cannot inherently model token order, positional encodings are added to token embeddings. Early models used sinusoidal encodings, and later learnable embeddings were introduced. However, these approaches have several drawbacks: They encode absolute rather than relative positions, reducing contextual precision Struggle with generalizing to sequences of varying lengths Increase model parameters and often degrade in long-range dependencies ...

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...

What is a Transformer? The Transformer is a neural network architecture introduced by Vaswani et al. in the 2017 paper "Attention is All You Need." It revolutionized natural language processing by replacing sequential models like RNNs and LSTMs. Transformers process entire sentences in parallel using self-attention , effectively addressing the difficulty of learning from long input sequences and enabling high computational efficiency by overcoming the limitations of sequential processing. 1. Transformer Components and Overcoming RNN/LSTM Limitations The Transformer is composed of an encoder and a decoder, with each block consisting of the following key components: Self-Attention: Learns the relationships between tokens within the input sequence by enabling each token to attend to all others, effectively capturing long-range dependencies and rich contextual information. Multi-Head Attention (MHA): Divides self-attention into multiple parallel heads. Each head focuses o...