--- title: "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 source_url: https://code.likeagirl.io/how-self-attention-works-in-large-language-models-explained-with-simple-examples-985baa8eb6bf type: article source: "Code Like A Girl" published: 2026-08-14T11:42:02.652Z updated: 2026-08-14T12:02:25.636Z tags: ["machine-learning", "llm", "neural-networks", "transformers"] reading_time: 8 upvotes: 4 comments: 0 language: 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 intuitive purpose of self-attention in transformer models? Self-attention lets every token in a sequence weigh how relevant every other token is before making a prediction, rather than reading strictly left to right. For example, in 'The cat sat on the mat,' when processing 'sat' the model might assign roughly 60% attention to 'cat,' 20% to 'sat' itself, 10% to 'on,' and smaller amounts to 'the' and 'mat,' letting it capture relationships between distant words. _daily.dev surfaces explainers like this for developers building intuition around transformer internals._ ### What do Query, Key, and Value mean in self-attention? Query, Key, and Value are vectors each token produces to determine attention: the Query describes what a token is looking for, the Key describes what information a token offers, and the Value holds the information actually shared once a match is found. It works like a library search where your search request is the Query, book labels are Keys, and book contents are the Values retrieved. _Developers piecing together transformer mechanics can keep exploring related concepts on daily.dev._ ### Why do transformers need positional encoding alongside self-attention? Positional encoding is needed because self-attention on its own has no concept of word order, since every token compares against every other token simultaneously regardless of sequence. Without it, sentences like 'Dog bites man' and 'Man bites dog' would look nearly identical to the model, so positional encoding tags each token with its position before attention is computed. _daily.dev helps developers connect foundational AI concepts like this while learning how LLMs are built._ ## Similar posts on daily.dev - [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 --- 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)