How LLMs Break Text Into Tokens | Byte Pair Encoding Explained Visually

How do large language models turn raw text into tokens? In this video, we break down Byte Pair Encoding (BPE) step by step and show how tokenization works inside modern LLM pipelines. Using an interactive demo, we walk through how BPE starts from characters, repeatedly merges the most frequent adjacent pairs, builds reusable subword tokens, and creates the tradeoff between vocabulary size and sequence length. 🔗Link to demo: https://gyms.schovia.com/BPE/bpe-demo You'll see: - how BPE tokenization works - why LLMs use subword tokens instead of only words or only characters - how frequent pair merging builds a tokenizer vocabulary - why vocabulary size goes up while context length goes down - how BPE helps with rare words, names, misspellings, and domain-specific terms - why tokenization affects efficiency, memory, and model behavior ⏱ Timestamps: 0:00 — Introduction 0:39 — The problem BPE solves 1:03 — Word-level vs character-level tokenization 2:15 — How BPE works: the core idea 2:49 — Step-by-step merging example 4:03 — Frequency-driven merging explained 4:29 — Watching BPE build tokens in the demo 5:04 — Reusable chunks and the merge table 5:46 — The vocabulary size vs context length tradeoff 6:07 — Interactive demo recap 6:14 — Why BPE helps with rare and unknown words 7:27 — The limits of merging: when to stop 7:55 — Key takeaway: subword vocabulary by frequency 8:28 — BPE's role before an LLM predicts tokens 8:56 — Wrap-up This video is designed for engineers, technical practitioners, students, and decision-makers who want a clearer mental model of how language models actually process text. If you've heard terms like tokenization, BPE, subword tokenization, context window, or vocabulary size and wanted an intuitive explanation, this walkthrough is for you. #BytePairEncoding #BPE #Tokenization #LLM #LargeLanguageModels #NLP #NaturalLanguageProcessing #SubwordTokenization #MachineLearning #AI #ArtificialIntelligence #DeepLearning #ContextWindow #VocabularySize #AIExplained #Schovia