How Tokenization and Embeddings Actually Works In LLMs | Deep Dive

2026-03-24 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This article details how Large Language Models (LLMs) process natural language into numerical representations, focusing on tokenization and embeddings. It explains that while tokenization breaks sentences into sub-words and assigns numerical IDs, it lacks semantic meaning. To address this, token embeddings are introduced, which represent words as vectors in an n-dimensional space, capturing semantic relationships. The article demonstrates this concept with a simplified feature-based ranking for objects like "cat" and "dog," and then practically applies it using the `word2vec-google-news-300` model in Python with `gensim`. It further explains how LLMs create embedding layers, citing GPT-2's 50,257 vocabulary size and 768 vector dimension, resulting in a 50,257*768 embedding layer weight matrix. Finally, it covers positional embeddings, which add unique positional information to word embeddings, enabling Transformer architectures to understand word order despite parallel processing.

Key takeaway

For AI Engineers building or fine-tuning LLMs, understanding the interplay between tokenization, semantic embeddings, and positional encoding is crucial. Your choice of tokenizer and embedding model directly impacts the model's ability to grasp context and meaning. Ensure your architecture correctly integrates positional information to prevent loss of sequence understanding, especially in Transformer-based models, which process tokens in parallel.

Key insights

Tokenization and embeddings convert natural language into numerical representations, with positional embeddings preserving word order for LLMs.

Principles

• Tokenization alone is insufficient for semantic understanding.
• Embeddings capture semantic relationships between words.
• Positional encoding provides sequence order to parallel-processed tokens.

Method

Generate token embeddings by training a neural network to assign vector representations, then add sinusoidal positional encodings to preserve word order for Transformer models.

In practice

• Use `gensim` to load pre-trained embedding models like `word2vec-google-news-300`.
• Implement `torch.nn.Embedding` for custom embedding layers in PyTorch.
• Visualize embedding relationships using PCA for dimensionality reduction.

Topics

• Tokenization
• Word Embeddings
• Positional Encoding
• Large Language Models
• Transformer Architecture

Best for: AI Engineer, Machine Learning Engineer, Deep Learning Engineer

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• What is an LLM? Tokens, Embeddings, and the Big Picture — LLMs operate on subword tokens, not characters, making tokenization a core determinant of their capabilities and predictable failure points.
• How Transformers Architecture Powers Modern LLMs — LLMs use a transformer architecture to convert text into numerical representations, process context, and predict the next token.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.