<?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>AI Engineering Posts on Data &amp; AI School</title><link>https://dataandaischool.io/ai-engineering/posts/</link><description>Recent content in AI Engineering Posts on Data &amp; AI School</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 20 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dataandaischool.io/ai-engineering/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Getting an LLM to Follow the Rules: Reliable Structured Output</title><link>https://dataandaischool.io/ai-engineering/posts/reliable-structured-output/</link><pubDate>Thu, 20 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/reliable-structured-output/</guid><description>Why &amp;lsquo;just ask for JSON&amp;rsquo; isn&amp;rsquo;t enough, and the real techniques — schemas, function calling, output parsers — for getting consistent, parseable output every time.</description></item><item><title>Prompting Like a Pro (Not Like a Google Search)</title><link>https://dataandaischool.io/ai-engineering/posts/prompting-like-a-pro/</link><pubDate>Thu, 20 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/prompting-like-a-pro/</guid><description>The named prompt engineering patterns practitioners actually use — chain-of-thought, role prompting, and self-consistency — explained simply.</description></item><item><title>Stop Copy-Pasting Prompts: Building a Prompt Library That Scales</title><link>https://dataandaischool.io/ai-engineering/posts/prompt-templates-and-libraries/</link><pubDate>Thu, 20 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/prompt-templates-and-libraries/</guid><description>Moving from one-off prompts scattered in code to versioned, parameterized templates you can actually test, reuse, and maintain.</description></item><item><title>Evaluating GenAI Systems (Why 'It Feels Good' Isn't Enough)</title><link>https://dataandaischool.io/ai-engineering/posts/evaluating-genai-systems/</link><pubDate>Wed, 19 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/evaluating-genai-systems/</guid><description>GenAI systems need real evaluation — accuracy, relevance, safety — just like any other engineering system, not vibes-based judgment.</description></item><item><title>AI Agents 101: From Chatbot to Agent</title><link>https://dataandaischool.io/ai-engineering/posts/ai-agents-101/</link><pubDate>Tue, 18 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/ai-agents-101/</guid><description>The shift from a model that answers questions to one that takes real actions using tools — the plan, act, observe, repeat loop.</description></item><item><title>Hallucinations: Why They Happen and How to Reduce Them</title><link>https://dataandaischool.io/ai-engineering/posts/hallucinations-why-and-how-to-reduce/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/hallucinations-why-and-how-to-reduce/</guid><description>The honest explanation for why LLMs make things up, and practical ways to reduce it — grounding, citations, and a few other levers.</description></item><item><title>Embeddings and Vector Similarity</title><link>https://dataandaischool.io/ai-engineering/posts/embeddings-and-vector-similarity/</link><pubDate>Wed, 05 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/embeddings-and-vector-similarity/</guid><description>How text gets turned into numbers that capture meaning, and why &amp;lsquo;similar meaning = nearby numbers&amp;rsquo; is the foundation of search, recommendations, and RAG.</description></item><item><title>Prompting Fundamentals</title><link>https://dataandaischool.io/ai-engineering/posts/prompting-fundamentals/</link><pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/prompting-fundamentals/</guid><description>Why specificity beats cleverness, and how to talk to a model that takes everything you say literally.</description></item><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><item><title>What Is Generative AI (and What It Isn't)</title><link>https://dataandaischool.io/ai-engineering/posts/what-is-generative-ai/</link><pubDate>Sat, 01 Aug 2026 00:00:00 +0000</pubDate><guid>https://dataandaischool.io/ai-engineering/posts/what-is-generative-ai/</guid><description>The simple difference between predictive AI and generative AI, explained without the jargon.</description></item></channel></rss>