{"id":135567,"date":"2026-08-27T22:45:21","date_gmt":"2026-08-27T17:15:21","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=135567"},"modified":"2026-08-27T22:45:24","modified_gmt":"2026-08-27T17:15:24","slug":"what-is-tokenization","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/what-is-tokenization\/","title":{"rendered":"What is Tokenization Explained: How LLMs Read Text"},"content":{"rendered":"\n<p>Before an AI model can \u201cunderstand\u201d language, it has to break text into tiny, meaningful pieces. That step is <strong>tokenization<\/strong>, and it is the very first thing that happens when you send a prompt to any large language model.<\/p>\n\n\n\n<p>Without tokenization, an LLM would just see a string of characters. With it, the model sees a structured sequence of tokens that it has learned to reason about during training.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Direct Answer<\/strong><\/h3>\n\n\n\n<p><strong>Tokenization in LLMs<\/strong> is the process of converting raw text into smaller units called tokens, which the model can process. A token can be a word, part of a word (subword), a character, or punctuation. The tokenizer maps each token to a unique integer ID, turning text into a sequence of numbers that the transformer can understand. This step is essential because LLMs cannot read raw text directly; they operate on token IDs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>TL;DR Summary<\/strong><\/h3>\n\n\n\n<ul>\n<li>Tokenization splits text into tokens (words, subwords, or characters) that LLMs can process.<\/li>\n\n\n\n<li>Each token is mapped to a unique integer ID in the model\u2019s vocabulary.<\/li>\n\n\n\n<li>Modern LLMs mostly use subword tokenization like BPE and WordPiece.<\/li>\n\n\n\n<li>Tokenization affects context length, cost, and how the model understands rare words.<\/li>\n\n\n\n<li>Good tokenization balances vocabulary size, coverage, and efficiency.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Tokenization in LLMs?<\/strong><\/h2>\n\n\n\n<p><strong>Tokenization in LLMs<\/strong> is the process of converting raw text into a sequence of tokens. A token is the basic unit of text that the model processes. It can be:<\/p>\n\n\n\n<ul>\n<li>A full word (for example, \u201ccat\u201d).<\/li>\n\n\n\n<li>A subword fragment (for example, \u201cing\u201d, \u201ction\u201d, \u201cun\u201d).<\/li>\n\n\n\n<li>A single character.<\/li>\n\n\n\n<li>Punctuation or whitespace.<\/li>\n<\/ul>\n\n\n\n<p>The tokenizer does two things:<\/p>\n\n\n\n<ol>\n<li>Splits the input text into tokens.<\/li>\n\n\n\n<li>Maps each token to a unique integer ID from a fixed vocabulary.<\/li>\n<\/ol>\n\n\n\n<p>These IDs are what the transformer actually sees. The model never operates directly on raw strings.<\/p>\n\n\n\n<p><em>Tokenization splits text into subword tokens and maps them to numbers so LLMs can process language efficiently. Learn AI &amp; ML with HCL GUVI\u2019s<\/em><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning?utm_source=Tokenization-Explained-How-LLMs-Read-Text\" target=\"_blank\" rel=\"noreferrer noopener\"><em> Artificial Intelligence and Machine Learning course<\/em><\/a><em>.&nbsp;<\/em><\/p>\n\n\n\n<figure class=\"wp-block-pullquote\"><blockquote><p>\ud83d\udca1 <strong>Pro Tip:<\/strong> Think of tokenization as translating human-readable text into the model\u2019s native \u201clanguage\u201d of numbers.<\/p><\/blockquote><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Do LLMs Need Tokenization?<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/llm-skills\/\" target=\"_blank\" rel=\"noreferrer noopener\">LLMs <\/a>cannot read text the way humans do. They are mathematical models that operate on sequences of integers.<\/p>\n\n\n\n<p>Tokenization matters because it:<\/p>\n\n\n\n<ul>\n<li>Converts text into a format the model can process (token IDs).<\/li>\n\n\n\n<li>Defines the model\u2019s vocabulary and how it represents words.<\/li>\n\n\n\n<li>Determines how many tokens a sentence uses, which affects context limits and cost.<\/li>\n\n\n\n<li>Influences how well the model handles rare or unseen words.<\/li>\n<\/ul>\n\n\n\n<p>If tokenization is poor, the model struggles with uncommon words, misspellings, or mixed languages. Good tokenization helps the model generalize better.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How Tokenization Works: Step by Step<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"940\" height=\"537\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/image-534.png\" alt=\"How Tokenization Works: Step by Step\" class=\"wp-image-135569\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/image-534.png 940w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/image-534-300x171.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/image-534-768x439.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/image-534-150x86.png 150w\" sizes=\"(max-width: 940px) 100vw, 940px\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Raw Text Input<\/strong><\/h3>\n\n\n\n<p>You send a <a href=\"https:\/\/www.guvi.in\/blog\/what-is-prompt-tuning\/\" target=\"_blank\" rel=\"noreferrer noopener\">prompt <\/a>like:<\/p>\n\n\n\n<p>\u201cTokenization is important.\u201d<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Splitting Into Tokens<\/strong><\/h3>\n\n\n\n<p>The tokenizer breaks this into pieces. Depending on the algorithm, it might become:<\/p>\n\n\n\n<ul>\n<li>Word-level: [&#8220;Tokenization&#8221;, &#8220;is&#8221;, &#8220;important&#8221;, &#8220;.&#8221;]<\/li>\n\n\n\n<li>Subword-level: [&#8220;Token&#8221;, &#8220;ization&#8221;, &#8220;is&#8221;, &#8220;im&#8221;, &#8220;port&#8221;, &#8220;ant&#8221;, &#8220;.&#8221;]<\/li>\n\n\n\n<li>Character-level: [&#8220;T&#8221;, &#8220;o&#8221;, &#8220;k&#8221;, &#8220;e&#8221;, &#8220;n&#8221;, &#8220;i&#8221;, &#8220;z&#8221;, &#8220;a&#8221;, &#8220;t&#8221;, &#8220;i&#8221;, &#8220;o&#8221;, &#8220;n&#8221;, &#8230;]<\/li>\n<\/ul>\n\n\n\n<p>Most modern LLMs use subword tokenization, which balances vocabulary size and coverage.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Mapping to Token IDs<\/strong><\/h3>\n\n\n\n<p>Each token is looked up in a vocabulary table and replaced with an integer ID.<\/p>\n\n\n\n<p>For example:<\/p>\n\n\n\n<ul>\n<li>\u201cToken\u201d \u2192 1234<\/li>\n\n\n\n<li>\u201cization\u201d \u2192 5678<\/li>\n\n\n\n<li>\u201cis\u201d \u2192 42<\/li>\n\n\n\n<li>\u201cim\u201d \u2192 910<\/li>\n\n\n\n<li>\u201cport\u201d \u2192 112<\/li>\n\n\n\n<li>\u201cant\u201d \u2192 314<\/li>\n\n\n\n<li>\u201c.\u201d \u2192 17<\/li>\n<\/ul>\n\n\n\n<p>The model then sees:<\/p>\n\n\n\n<p>[1234, 5678, 42, 910, 112, 314, 17]<\/p>\n\n\n\n<p>This numeric sequence is what flows through the transformer layers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: Generating Output Tokens<\/strong><\/h3>\n\n\n\n<p>When the model generates text, it outputs token IDs one by one. The tokenizer then converts those IDs back into human-readable text.<\/p>\n\n\n\n<p>This is why LLMs generate text token-by-token, not word-by-word.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Types of Tokenization<\/strong><\/h2>\n\n\n\n<p>There are several ways to split text into tokens. Each has trade-offs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Word Tokenization<\/strong><\/h3>\n\n\n\n<p>Splits text into whole words.<\/p>\n\n\n\n<ul>\n<li>Example: [&#8220;Tokenization&#8221;, &#8220;is&#8221;, &#8220;important&#8221;]<\/li>\n\n\n\n<li>Pros: Simple, human-readable.<\/li>\n\n\n\n<li>Cons: Large vocabulary, poor handling of rare or misspelled words, struggles with new words not in the vocabulary.<\/li>\n<\/ul>\n\n\n\n<p>Word tokenization is rarely used in modern LLMs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Character Tokenization<\/strong><\/h3>\n\n\n\n<p>Splits text into individual characters.<\/p>\n\n\n\n<ul>\n<li>Example: [&#8220;T&#8221;, &#8220;o&#8221;, &#8220;k&#8221;, &#8220;e&#8221;, &#8220;n&#8221;, &#8220;i&#8221;, &#8220;z&#8221;, &#8220;a&#8221;, &#8220;t&#8221;, &#8220;i&#8221;, &#8220;o&#8221;, &#8220;n&#8221;, &#8230;]<\/li>\n\n\n\n<li>Pros: Small vocabulary, can represent any word.<\/li>\n\n\n\n<li>Cons: Very long sequences, weak semantic meaning per token, harder for the model to learn patterns.<\/li>\n<\/ul>\n\n\n\n<p>Character tokenization is useful for some tasks but not ideal for large-scale l<a href=\"https:\/\/mark-riedl.medium.com\/a-very-gentle-introduction-to-large-language-models-without-the-hype-5f67941fa59e\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">anguage modeling.<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Subword Tokenization<\/strong><\/h3>\n\n\n\n<p>Splits words into meaningful subword units.<\/p>\n\n\n\n<ul>\n<li>Example: [&#8220;Token&#8221;, &#8220;ization&#8221;, &#8220;is&#8221;, &#8220;im&#8221;, &#8220;port&#8221;, &#8220;ant&#8221;]<\/li>\n\n\n\n<li>Pros: Balances vocabulary size and coverage, handles rare words well, efficient sequences.<\/li>\n\n\n\n<li>Cons: More complex to implement, tokens are less human-intuitive.<\/li>\n<\/ul>\n\n\n\n<p>Subword tokenization is the standard in modern LLMs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Popular Subword Tokenization Algorithms<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Byte Pair Encoding (BPE)<\/strong><\/h3>\n\n\n\n<p><strong>BPE<\/strong> starts with a character-level vocabulary and iteratively merges the most frequent adjacent token pairs.<\/p>\n\n\n\n<ul>\n<li>Frequent pairs like \u201cing\u201d, \u201ction\u201d, or \u201cun\u201d become single tokens.<\/li>\n\n\n\n<li>Rare words are represented as combinations of subwords.<\/li>\n\n\n\n<li>Used in GPT-style models.<\/li>\n<\/ul>\n\n\n\n<p>BPE offers a good balance between vocabulary size and the ability to represent rare or unseen words.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>WordPiece<\/strong><\/h3>\n\n\n\n<p><strong>WordPiece<\/strong> is similar to BPE but uses a probabilistic approach to decide which subwords to merge.<\/p>\n\n\n\n<ul>\n<li>It is used in models like BERT.<\/li>\n\n\n\n<li>Often prepends special markers (like \u201c\u2581\u201d) to indicate word boundaries.<\/li>\n\n\n\n<li>Works well for multilingual and masked language modeling tasks.<\/li>\n<\/ul>\n\n\n\n<p>WordPiece is another widely adopted subword tokenization method.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>SentencePiece<\/strong><\/h3>\n\n\n\n<p><strong>SentencePiece<\/strong> treats the entire sentence as a sequence and can operate directly on raw text without pre-tokenization.<\/p>\n\n\n\n<ul>\n<li>Useful for languages without clear word boundaries (for example, Chinese, Japanese).<\/li>\n\n\n\n<li>Supports both BPE-like and unigram-based tokenization.<\/li>\n\n\n\n<li>Common in multilingual and sequence-to-sequence models.<\/li>\n<\/ul>\n\n\n\n<p>SentencePiece is popular when working with diverse languages and non-Latin scripts.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Tiktoken and Model-Specific Tokenizers<\/strong><\/h2>\n\n\n\n<p>Some models use custom tokenizers optimized for their training data and architecture.<\/p>\n\n\n\n<ul>\n<li>OpenAI\u2019s <strong>tiktoken<\/strong> is used in <a href=\"https:\/\/www.guvi.in\/blog\/what-are-gpt-assistants\/\" target=\"_blank\" rel=\"noreferrer noopener\">GPT<\/a>-4 and related models.<\/li>\n\n\n\n<li>These tokenizers are tuned for efficiency and specific token distributions.<\/li>\n\n\n\n<li>They often have large vocabularies (tens of thousands to over 100k tokens).<\/li>\n<\/ul>\n\n\n\n<p>When working with a specific model, it is important to use its official tokenizer to match token counts and behavior.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Tokenization Matters in Practice<\/strong><\/h2>\n\n\n\n<p>Tokenization is not just a technical detail. It affects real-world behavior and costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Context Length and Token Limits<\/strong><\/h3>\n\n\n\n<p>LLMs have a maximum context length measured in tokens, not characters or words.<\/p>\n\n\n\n<ul>\n<li>A 128k-token model can handle more text with efficient tokenization.<\/li>\n\n\n\n<li>Poor tokenization can waste tokens on unnecessary splits.<\/li>\n<\/ul>\n\n\n\n<p>Understanding tokenization helps you estimate how much text fits in a prompt.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Pricing and Cost<\/strong><\/h3>\n\n\n\n<p>Many APIs charge per token (input + output).<\/p>\n\n\n\n<ul>\n<li>Better tokenization can reduce token count for the same content.<\/li>\n\n\n\n<li>Subword tokenization usually compresses common patterns better than character-level approaches.<\/li>\n<\/ul>\n\n\n\n<p>If you build applications on top of LLMs, tokenization directly impacts your costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Handling Rare and Unseen Words<\/strong><\/h3>\n\n\n\n<p>Good subword tokenization ensures that rare or new words can still be represented.<\/p>\n\n\n\n<ul>\n<li>Instead of \u201cunknown,\u201d the model sees familiar subword pieces.<\/li>\n\n\n\n<li>This improves robustness for misspellings, domain-specific terms, and new words.<\/li>\n<\/ul>\n\n\n\n<p>This is one reason subword methods dominate modern LLMs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Multilingual Support<\/strong><\/h3>\n\n\n\n<p>Tokenizers designed for multilingual use handle many scripts and languages.<\/p>\n\n\n\n<ul>\n<li>They learn subword units across languages.<\/li>\n\n\n\n<li>This helps the model share representations and generalize better.<\/li>\n<\/ul>\n\n\n\n<p>SentencePiece and similar tokenizers are common in multilingual models.<\/p>\n\n\n\n<p>\u26a0\ufe0f <strong>Warning:<\/strong> Do not assume that token counts match word counts. A 100-word prompt can easily become 130\u2013150 tokens depending on the tokenizer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Mistakes to Avoid<\/strong><\/h2>\n\n\n\n<ul>\n<li>Assuming tokens are the same as words.<\/li>\n\n\n\n<li>Ignoring tokenization when estimating prompt length or API costs.<\/li>\n\n\n\n<li>Mixing tokenizers from different models and expecting consistent behavior.<\/li>\n\n\n\n<li>Overlooking special tokens (for example, &lt;bos&gt;, &lt;eos&gt;, &lt;pad&gt;) in model inputs.<\/li>\n\n\n\n<li>Forgetting that tokenization affects how the model sees misspellings and rare terms.<\/li>\n\n\n\n<li>Not testing tokenization on your actual data before deploying.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-pullquote\"><blockquote><p>\u2705 <strong>Best Practice:<\/strong> Always use the official tokenizer for the model you are working with. For OpenAI models, use tiktoken. For Hugging Face models, use the tokenizer provided with the model card.<\/p><\/blockquote><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What to Do Next<\/strong><\/h2>\n\n\n\n<p>If you want to understand tokenization more deeply:<\/p>\n\n\n\n<ul>\n<li>Install a tokenizer library (for example, Hugging Face transformers or OpenAI tiktoken).<\/li>\n\n\n\n<li>Tokenize sample sentences and inspect the token IDs and decoded text.<\/li>\n\n\n\n<li>Compare how different tokenizers split the same text.<\/li>\n\n\n\n<li>Count tokens in your typical prompts to estimate context usage and cost.<\/li>\n\n\n\n<li>Read documentation for your chosen model\u2019s tokenizer to learn about special tokens and edge cases.<\/li>\n<\/ul>\n\n\n\n<p>Tokenization is a foundational concept for working with LLMs. A clear mental model of how text becomes tokens will make you better at prompt engineering, debugging, and building AI applications.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \n   Most modern LLMs use subword tokenization, which means a single word can be represented by multiple tokens (for example, \u201cunbelievable\u201d \u2192 \u201cun\u201d, \u201cbeliev\u201d, \u201cable\u201d).\nToken counts are not universal: the same sentence can have different token counts depending on which model\u2019s tokenizer you use.\n<\/div>\n\n\n\n<p><em>Tokenization splits text into subword tokens and maps them to numbers so LLMs can process language efficiently. Learn AI &amp; ML with HCL GUVI\u2019s<\/em><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning?utm_source=Tokenization-Explained-How-LLMs-Read-Text\" target=\"_blank\" rel=\"noreferrer noopener\"><em> Artificial Intelligence and Machine Learning course<\/em><\/a><em>.&nbsp;<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p><strong>Tokenization in LLMs<\/strong> is the process of converting raw text into tokens and then into integer IDs that the model can process. Tokens can be words, subwords, or characters, with subword methods like BPE and WordPiece being the most common in modern models.<\/p>\n\n\n\n<p>Tokenization defines the model\u2019s vocabulary, affects context length and cost, and determines how well the model handles rare and unseen words. Understanding tokenization is essential for anyone working with LLMs, from prompt engineers to application developers.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1787720109232\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is tokenization in LLMs?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Tokenization in LLMs is the process of splitting text into tokens (words, subwords, or characters) and mapping them to integer IDs that the model can process.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720115342\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is a token in an LLM?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A token is the basic unit of text that an LLM processes. It can be a word, part of a word, a character, or punctuation, depending on the tokenization method.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720123454\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Why do LLMs use tokenization?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>LLMs use tokenization to convert text into numeric sequences. They cannot operate on raw strings, so tokenization translates human language into a format the model understands.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720148025\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is subword tokenization?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Subword tokenization splits words into meaningful fragments (for example, \u201cun\u201d, \u201cbeliev\u201d, \u201cable\u201d). It balances vocabulary size and coverage, and handles rare words better than word-level tokenization.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720156868\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What are BPE and WordPiece?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>BPE (Byte Pair Encoding) and WordPiece are popular subword tokenization algorithms. BPE iteratively merges frequent token pairs, while WordPiece uses a probabilistic approach. Both are widely used in LLMs.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720163648\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Does tokenization affect token limits and cost?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Tokenization determines how many tokens a given text produces, which directly affects context usage and API pricing in token-based billing models.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787720208991\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Do all LLMs use the same tokenizer?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>No. Different models use different tokenizers and vocabularies. The same sentence can have different token counts depending on the model and tokenizer used.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Before an AI model can \u201cunderstand\u201d language, it has to break text into tiny, meaningful pieces. That step is tokenization, and it is the very first thing that happens when you send a prompt to any large language model. Without tokenization, an LLM would just see a string of characters. With it, the model sees [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":135955,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"27","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Tokenization-Explained-How-LLMs-Read-Text-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/135567"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=135567"}],"version-history":[{"count":6,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/135567\/revisions"}],"predecessor-version":[{"id":136025,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/135567\/revisions\/136025"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/135955"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=135567"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=135567"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=135567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}