--- title: Embeddings: The Reason Machines Finally “Get” Language description: A beginner-friendly explainer on word embeddings, tracing the evolution from Bag of Words to Word2Vec and modern LLMs. Covers what embeddings are (words as... canonical: https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Embeddings: The Reason Machines Finally “Get” Language | daily.dev og:description: A beginner-friendly explainer on word embeddings, tracing the evolution from Bag of Words to Word2Vec and modern LLMs. Covers what embeddings are (words as... og:url: https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k og:image: https://api.daily.dev/og/posts/UBxzJ3f3k.png og:image:alt: Embeddings: The Reason Machines Finally “Get” Language og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Embeddings: The Reason Machines Finally “Get” Language **[Medium](https://daily.dev/sources/medium_js)** · 8 min read · 1 upvotes · 0 comments ## Summary A beginner-friendly explainer on word embeddings, tracing the evolution from Bag of Words to Word2Vec and modern LLMs. Covers what embeddings are (words as points in vector space), the different types (word, sentence, image, audio, multimodal), how they are created via neural network training, and why they were a turning point for NLP. Also explores real-world applications including search engines, recommendation systems, and social media feeds like Instagram Explore and X's For You feed. Ends with a teaser about the limitation of static embeddings for polysemous words like 'bank'. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between Bag of Words and word embeddings like Word2Vec? Bag of Words simply counts word occurrences with no understanding of context, so it treats identical words like 'bank' in 'river bank' and 'bank loan' as the same thing. Word2Vec, introduced in 2013, instead represents words as points in a numerical space where semantically similar words end up close together, capturing meaning rather than just tallying text. _For deeper explainers on how NLP models evolved from counting to meaning, daily.dev surfaces posts developers are reading on embeddings._ ### What are the main types of embeddings used in machine learning? There are five common types: word embeddings (one point per word, as in Word2Vec), sentence or document embeddings (a whole passage as one point), image embeddings (visual content mapped to points), audio embeddings (sound clips mapped by underlying meaning rather than raw waveform), and multimodal embeddings, which map text and images into the same shared space so a caption and its matching photo land close together. _Developers comparing embedding approaches for a project can find related discussions curated on daily.dev._ ### How do embeddings enable next token prediction in large language models? When text is entered into a language model, the words are first converted into embeddings, so the model reads points in a meaning-space rather than literal letters. Next token prediction then works by looking at the embedding of everything said so far and selecting the nearby point in that space most likely to come next, rather than matching exact words. _Anyone learning how LLMs generate text can track deeper breakdowns of this mechanism on daily.dev._ ## Similar posts on daily.dev - [Embeddings Explained: How AI Understands Meaning](https://daily.dev/posts/embeddings-explained-how-ai-understands-meaning-ixv4nljq5) · Medium · 0 upvotes · 0 comments - [Vector embeddings & language: how models turn words into geometry](https://daily.dev/posts/vector-embeddings-language-how-models-turn-words-into-geometry-io9eekrza) · Redis · 0 upvotes · 0 comments - [The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel](https://daily.dev/posts/the-machine-learning-advent-calendar-day-22-embeddings-in-excel-szp1wvqrr) · Towards Data Science · 0 upvotes · 0 comments - [AI Foundations Series — Part 1](https://daily.dev/posts/ai-foundations-series-part-1-kntu2wgux) · Medium · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#nlp](https://daily.dev/tags/nlp), [#embeddings](https://daily.dev/tags/embeddings) [View this post on daily.dev](https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Embeddings: The Reason Machines Finally “Get” Language","url":"https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k"},"datePublished":"2026-07-21T15:45:22.151Z","dateModified":"2026-09-14T07:02:37.550Z","description":"A beginner-friendly explainer on word embeddings, tracing the evolution from Bag of Words to Word2Vec and modern LLMs. Covers what embeddings are (words as...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/3a30ce922d656fb5a194ab293b3d363b?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/3a30ce922d656fb5a194ab293b3d363b?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm,nlp,embeddings","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Embeddings: The Reason Machines Finally “Get” Language"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/embeddings-the-reason-machines-finally-get-language-ubxzj3f3k#faq","mainEntity":[{"@type":"Question","name":"What is the difference between Bag of Words and word embeddings like Word2Vec?","acceptedAnswer":{"@type":"Answer","text":"Bag of Words simply counts word occurrences with no understanding of context, so it treats identical words like 'bank' in 'river bank' and 'bank loan' as the same thing. 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