<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Llm on Data &amp; AI School</title><link>https://dataandaischool.io/tags/llm/</link><description>Recent content in Llm on Data &amp; AI School</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 03 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dataandaischool.io/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Tokens, Context Windows, and Why They Matter</title><link>https://dataandaischool.io/ai-engineering/posts/tokens-and-context-windows/</link><pubDate>Mon, 03 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/tokens-and-context-windows/</guid><description>What a token actually is, why every model has a memory limit, and why this quietly affects cost, speed, and what you can even ask a model to do.</description></item><item><title>How LLMs Actually Work (Next-Token Prediction, Simply Explained)</title><link>https://dataandaischool.io/ai-engineering/posts/how-llms-actually-work/</link><pubDate>Sun, 02 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/how-llms-actually-work/</guid><description>No math-heavy transformer internals, just the core idea: an LLM is a very sophisticated autocomplete engine trained on huge amounts of text.</description></item></channel></rss>