--- title: How Self-Attention Works in Large Language Models:... description: An intuitive, math-light explanation of how self-attention works in Transformer-based language models. Uses everyday analogies (a meeting, a library search) to... canonical: https://daily.dev/posts/how-self-attention-works-in-large-language-models-explained-with-simple-examples-g2g9mklnw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How Self-Attention Works in Large Language Models: Explained with Simple Examples | daily.dev og:description: An intuitive, math-light explanation of how self-attention works in Transformer-based language models. Uses everyday analogies (a meeting, a library search) to... og:url: https://daily.dev/posts/how-self-attention-works-in-large-language-models-explained-with-simple-examples-g2g9mklnw og:image: https://api.daily.dev/og/posts/g2G9mKlNW.png og:image:alt: How Self-Attention Works in Large Language Models: Explained with Simple Examples 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. # How Self-Attention Works in Large Language Models: Explained with Simple Examples **[Code Like A Girl](https://daily.dev/sources/colkgirl)** · 8 min read · 4 upvotes · 0 comments ## Summary An intuitive, math-light explanation of how self-attention works in Transformer-based language models. Uses everyday analogies (a meeting, a library search) to explain why models weigh some tokens more than others, why positional encoding is needed, and how Query, Key, and Value vectors combine to determine attention scores. Also covers multi-head attention, self-attention's computational scaling limitation, and common misconceptions about what self-attention does and doesn't mean for model 'understanding'. ## 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 the Query, Key, and Value in self-attention? The Query represents what a token is looking for, the Key describes what information another token offers, and the Value contains the actual information that gets shared. The model compares every Query against every Key to compute a match score, and the better the match, the more that token's Value influences the final representation, similar to searching a library where your search request is the query, book labels are keys, and book contents are values. _Developers building intuition around transformer internals can explore related deep dives on daily.dev._ ### Why do transformers need positional encoding if they use self-attention? Self-attention alone doesn't understand word order because every token looks at every other token simultaneously without regard to sequence position. Positional encoding solves this by giving each token information about its position in the sentence before attention is calculated, so sentences like 'dog bites man' and 'man bites dog' aren't treated as nearly identical. _Anyone piecing together how transformer components fit can track explainers like this on daily.dev._ ### What is a limitation of self-attention in transformer models? Self-attention requires every token to compare itself with every other token in the sequence, so computation grows rapidly as input length increases. This scaling problem is why researchers keep developing more efficient attention mechanisms for handling long-context inputs. _Engineers evaluating long-context model trade-offs can follow this research thread on daily.dev._ ## Similar posts on daily.dev - [Medium](https://daily.dev/posts/medium-p64bynjtm) · Medium · 0 upvotes · 0 comments - [The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel](https://daily.dev/posts/the-machine-learning-advent-calendar-day-24-transformers-for-text-in-excel-38pg2g7xb) · Towards Data Science · 0 upvotes · 0 comments - [Multi-Head Attention Explained: Queries, Keys, and Values Made Simple](https://daily.dev/posts/multi-head-attention-explained-queries-keys-and-values-made-simple-6tda3mel9) · DigitalOcean Community · 0 upvotes · 0 comments - [Fundamentals of AI: Inside the transformer](https://daily.dev/posts/fundamentals-of-ai-inside-the-transformer-szwloqlw1) · Cisco · 0 upvotes · 0 comments - [How Large Language Models Actually Work — And Why You Should Care](https://daily.dev/posts/how-large-language-models-actually-work-and-why-you-should-care-oiklqnxi7) · Medium · 2 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#neural-networks](https://daily.dev/tags/neural-networks), [#transformers](https://daily.dev/tags/transformers) [View this post on daily.dev](https://daily.dev/posts/how-self-attention-works-in-large-language-models-explained-with-simple-examples-g2g9mklnw) ```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":"How Self-Attention Works in Large Language Models: Explained with Simple Examples","url":"https://daily.dev/posts/how-self-attention-works-in-large-language-models-explained-with-simple-examples-g2g9mklnw","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-self-attention-works-in-large-language-models-explained-with-simple-examples-g2g9mklnw"},"datePublished":"2026-08-14T11:42:02.652Z","dateModified":"2026-09-13T20:58:51.063Z","description":"An intuitive, math-light explanation of how self-attention works in Transformer-based language models. 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