{"id":6383,"date":"2026-02-05T09:48:18","date_gmt":"2026-02-05T17:48:18","guid":{"rendered":"https:\/\/www.visla.us\/blog\/?p=6383"},"modified":"2026-02-05T09:49:42","modified_gmt":"2026-02-05T17:49:42","slug":"what-is-an-llm","status":"publish","type":"post","link":"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/","title":{"rendered":"What is an LLM?"},"content":{"rendered":"\n<div class=\"wp-block-group has-base-2-background-color has-background has-global-padding is-layout-constrained wp-container-core-group-is-layout-1 wp-block-group-is-layout-constrained\" style=\"border-radius:20px;padding-top:var(--wp--preset--spacing--20);padding-right:var(--wp--preset--spacing--20);padding-bottom:var(--wp--preset--spacing--20);padding-left:var(--wp--preset--spacing--20)\">\n<h2 class=\"wp-block-heading is-style-asterisk\">Quick Answer: What is an LLM?<\/h2>\n\n\n\n<p>An LLM, or large language model, is software that predicts the next chunk of text based on the text you give it. It learns that skill by reading enormous amounts of writing during training, then it uses what it learned to generate, rewrite, summarize, and answer questions in plain language. Because it works in probabilities, it can sound confident even when it guesses, so you still need checks, sources, and guardrails. Modern LLM apps often add tools like document retrieval or web search, which can improve factuality, but those tools still don\u2019t guarantee correctness.<\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">The most straightforward definition<\/h2>\n\n\n\n<p>A large language model (LLM) is a statistical model that takes text as input and predicts what text should come next.<\/p>\n\n\n\n<p>That sounds small, but it scales into something surprisingly flexible. If you ask it to draft a sales email, it predicts the next tokens of a sales email. If you paste a contract clause and ask for risks, it predicts the next tokens of a risk review. If you ask for a plan, it predicts the next tokens of a plan.<\/p>\n\n\n\n<p>Here\u2019s the key: the model doesn\u2019t \u201clook up\u201d an answer the way a search engine does. It generates an answer by building a probability distribution over possible next tokens, step by step, until it reaches a stopping point.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What makes it \u201clarge\u201d<\/h2>\n\n\n\n<p>When people say \u201clarge,\u201d they usually mean three things:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Lots of parameters:<\/strong> The model stores what it learns inside millions to hundreds of billions of numeric weights. Those weights don\u2019t store sentences like a database does, but they do shape how the model maps input text to output text.<\/li>\n\n\n\n<li><strong>Lots of training data:<\/strong> Training works best when the model sees a wide range of writing styles, topics, and formats.<\/li>\n\n\n\n<li><strong>A big context window:<\/strong> The model reads a limited amount of text at once, called its context window. Bigger context windows let it keep more of your prompt, instructions, and documents \u201cin mind\u201d while it writes.<\/li>\n<\/ul>\n\n\n\n<p>In business terms, \u201clarge\u201d usually correlates with broader coverage, better instruction following, and better handling of messy inputs, but it also raises practical questions about cost, latency, privacy, and governance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A high-level view of how an LLM works<\/h2>\n\n\n\n<p>You don\u2019t need to know the math to use an LLM well, but it helps to understand the pipeline:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Tokenize the text.<\/strong> The system breaks your text into tokens, which often look like short word pieces.<\/li>\n\n\n\n<li><strong>Run the transformer.<\/strong> Most modern LLMs use a transformer architecture that lets the model pay attention to different parts of the input at once.<\/li>\n\n\n\n<li><strong>Predict the next token.<\/strong> The model produces a probability distribution over the next token.<\/li>\n\n\n\n<li><strong>Choose a token and repeat.<\/strong> The system picks the next token (sometimes greedily, sometimes with sampling), appends it, and repeats.<\/li>\n<\/ol>\n\n\n\n<p>Training usually happens in stages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pretraining:<\/strong> The model reads huge text corpora and learns to predict the next token across many domains.<\/li>\n\n\n\n<li><strong>Instruction tuning:<\/strong> Trainers fine-tune the model on examples that look more like real prompts and helpful responses.<\/li>\n\n\n\n<li><strong>Preference tuning (often <\/strong><a href=\"https:\/\/www.ibm.com\/think\/topics\/rlhf\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>RLHF<\/strong><\/a><strong>):<\/strong> Trainers collect human preferences between model outputs and push the model toward the styles people prefer, like being clearer, safer, and more aligned with instructions.<\/li>\n<\/ul>\n\n\n\n<p>That combination explains why modern systems often feel more like assistants than like raw autocomplete.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A small vocabulary table that clears up a lot of confusion<\/h2>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th>Term<\/th><th>What it means in plain English<\/th><th>Why it matters at work<\/th><\/tr><\/thead><tbody><tr><td>Token<\/td><td>A chunk of text the model processes<\/td><td>Affects cost, speed, and how the model \u201ccounts\u201d input and output<\/td><\/tr><tr><td>Context window<\/td><td>The maximum text the model can consider at once<\/td><td>Determines how much of a doc set, chat, or instructions it can track<\/td><\/tr><tr><td>Parameters<\/td><td>The learned weights inside the model<\/td><td>Often correlates with capability, but training quality matters too<\/td><\/tr><tr><td>Pretraining<\/td><td>Broad training on general text<\/td><td>Gives the model wide coverage and general language ability<\/td><\/tr><tr><td>Fine-tuning<\/td><td>Extra training for a task or style<\/td><td>Helps the model follow your org\u2019s tone, formats, or domain<\/td><\/tr><tr><td>Retrieval (RAG)<\/td><td>The app fetches relevant docs and feeds them into the prompt<\/td><td>Improves grounding and lets you update knowledge without retraining<\/td><\/tr><tr><td>Tool use<\/td><td>The app lets the model call functions (search, calculators, CRM)<\/td><td>Turns \u201ctext generator\u201d into \u201cworkflow engine,\u201d with guardrails<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u201cIsn\u2019t it just autocorrect?\u201d and the Markov chain debate<\/h2>\n\n\n\n<p>You\u2019ll hear two common takes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u201cIt\u2019s just fancy autocorrect.\u201d<\/li>\n\n\n\n<li>\u201cIt\u2019s just a fancy <a href=\"https:\/\/builtin.com\/machine-learning\/markov-chain\" target=\"_blank\" rel=\"noreferrer noopener\">Markov chain<\/a>.\u201d<\/li>\n<\/ul>\n\n\n\n<p>Both statements point at something real, and both miss something important.<\/p>\n\n\n\n<p>An LLM does predict what comes next, like <a href=\"https:\/\/support.google.com\/websearch\/answer\/7368877?hl=en#zippy=%2Cwhere-autocomplete-predictions-come-from\" target=\"_blank\" rel=\"noreferrer noopener\">autocomplete<\/a> does. The difference comes from scale and flexibility: a modern LLM learns patterns across many writing tasks and formats, and it can condition on long instructions, examples, and documents.<\/p>\n\n\n\n<p>A <a href=\"https:\/\/www.geeksforgeeks.org\/machine-learning\/markov-chain\/\" target=\"_blank\" rel=\"noreferrer noopener\">Markov chain<\/a> predicts the next state based only on a limited history. If you let the history grow without bound, the distinction blurs, because both systems model conditional probabilities. In practice, LLMs use a learned, high-dimensional representation of context, and the transformer lets them route \u201cattention\u201d across that context in ways a fixed-order Markov model can\u2019t match. So you can treat an LLM as a next-token predictor, but you shouldn\u2019t treat it as a simple one.<\/p>\n\n\n\n<p>Also, none of this settles the \u201cdoes it think?\u201d argument, and you don\u2019t need that debate to use the tool responsibly. For business decisions, you mostly care about capability, reliability, and controls.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why LLMs sometimes sound smart and sometimes fail<\/h2>\n\n\n\n<p>LLMs optimize for plausible continuation, not for truth.<\/p>\n\n\n\n<p>That design creates a predictable pattern:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>They excel at form and structure.<\/strong> They write clean prose, mimic tones, draft outlines, and transform text.<\/li>\n\n\n\n<li><strong>They often help with reasoning-like tasks.<\/strong> They can plan, compare options, and explain concepts, especially when you give constraints and examples.<\/li>\n\n\n\n<li><strong>They can still hallucinate.<\/strong> When the model doesn\u2019t \u201cknow\u201d something, it may still generate a fluent answer that stitches together likely-sounding pieces.<\/li>\n<\/ul>\n\n\n\n<p>Even \u201cweb-enabled\u201d systems can still fail. A tool might fetch the wrong page, retrieve irrelevant snippets, or mix sources. Sometimes the model also misreads what it retrieved, or it blends retrieved facts with guesses.<\/p>\n\n\n\n<p>So, treat the model as a partner that needs grounding, not as an oracle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u201cLLMs write boring text\u201d and \u201cLLMs can\u2019t create anything original\u201d<\/h2>\n\n\n\n<p>Those critiques made sense in specific eras and setups.<\/p>\n\n\n\n<p>Early systems often produced repetitive or generic text when developers used decoding methods like greedy selection or beam search. Researchers later showed that sampling methods and training changes could reduce that degeneration and improve diversity.<\/p>\n\n\n\n<p>On originality, LLMs recombine patterns from training data, but they don\u2019t simply copy-and-paste by default. They can generalize and produce novel combinations, and they can also memorize and regurgitate exact spans under certain conditions. That tension explains why people can use them for creative work and why privacy and IP governance still matter.<\/p>\n\n\n\n<p>The practical takeaway: you shouldn\u2019t assume everything the model writes counts as unique, and you also shouldn\u2019t assume it can only output bland filler.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why LLMs keep improving so fast<\/h2>\n\n\n\n<p>Progress tends to come from a few levers that compound:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>More and better data:<\/strong> Teams filter, deduplicate, and mix data more carefully.<\/li>\n\n\n\n<li><strong>Smarter scaling:<\/strong> Researchers study how to allocate compute across model size and training tokens.<\/li>\n\n\n\n<li><strong>Better alignment training:<\/strong> Instruction and preference tuning help models follow intent.<\/li>\n\n\n\n<li><strong>Better systems around the model:<\/strong> Retrieval, tools, evaluation harnesses, and monitoring matter as much as the raw model.<\/li>\n<\/ul>\n\n\n\n<p>This pace explains why old \u201calways true\u201d claims about LLM behavior often age badly. It also explains why you should evaluate tools in your own workflows instead of relying on vibes from last year.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A grounded way to use LLMs in business<\/h2>\n\n\n\n<p>If you want the benefits without the surprises, set up a workflow that assumes the model can draft fast but still needs checks.<\/p>\n\n\n\n<p><strong>Where LLMs usually shine<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>First drafts: emails, proposals, briefs, landing pages, scripts<\/li>\n\n\n\n<li>Transformation: summarize, reformat, translate, change tone<\/li>\n\n\n\n<li>Knowledge work support: brainstorm options, critique a plan, map pros and cons<\/li>\n\n\n\n<li>Extraction: pull structured fields from messy text, then validate<\/li>\n<\/ul>\n\n\n\n<p><strong>Where you should add extra guardrails<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>High-stakes facts (legal, medical, financial): require sources and human review<\/li>\n\n\n\n<li>Brand or compliance-sensitive writing: enforce templates and approvals<\/li>\n\n\n\n<li>Anything that touches private data: limit inputs, log access, and use the right deployment<\/li>\n<\/ul>\n\n\n\n<p><strong>A simple playbook that works in most orgs<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Start with a tight prompt: goal, audience, constraints, examples.<\/li>\n\n\n\n<li>Ask for uncertainty: \u201cList what you\u2019re least sure about.\u201d<\/li>\n\n\n\n<li>Ground it: add retrieval over your internal docs, or provide a source pack.<\/li>\n\n\n\n<li>Validate: spot-check numbers, names, dates, quotes, and policy claims.<\/li>\n\n\n\n<li>Keep a paper trail: save prompts, sources, and versions for audits.<\/li>\n<\/ol>\n\n\n\n<p>If you treat an LLM as a fast collaborator with a consistent failure mode, you\u2019ll get a lot of leverage and fewer unpleasant surprises.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Quick Answer: What is an LLM? An LLM, or large language model, is software that predicts the next chunk of text based on the text you give it. It learns that skill by reading enormous amounts of writing during training, then it uses what it learned to generate, rewrite, summarize, and answer questions in plain [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":6389,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[],"class_list":["post-6383","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guides"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is an LLM? - The Visla Blog<\/title>\n<meta name=\"description\" content=\"Learn what a large language model (LLM) is, how it generates text, why it can hallucinate, and how to use it safely in business.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is an LLM? - The Visla Blog\" \/>\n<meta property=\"og:description\" content=\"Learn what a large language model (LLM) is, how it generates text, why it can hallucinate, and how to use it safely in business.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\" \/>\n<meta property=\"og:site_name\" content=\"The Visla Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-05T17:48:18+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-05T17:49:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.visla.us\/wp-content\/uploads\/2026\/02\/Thumbnail-Draft-1-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"720\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"May Horiuchi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"May Horiuchi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\"},\"author\":{\"name\":\"May Horiuchi\",\"@id\":\"https:\/\/www.visla.us\/blog\/#\/schema\/person\/dcb20e581baf8b9574924cab20d6ae6d\"},\"headline\":\"What is an LLM?\",\"datePublished\":\"2026-02-05T17:48:18+00:00\",\"dateModified\":\"2026-02-05T17:49:42+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\"},\"wordCount\":1468,\"publisher\":{\"@id\":\"https:\/\/www.visla.us\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.visla.us\/wp-content\/uploads\/2026\/02\/Thumbnail-Draft-1-1.jpg\",\"articleSection\":[\"Guides\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\",\"url\":\"https:\/\/www.visla.us\/blog\/guides\/what-is-an-llm\/\",\"name\":\"What is an LLM? 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