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Vectors, Norms, and Dot Products Explained: The Math Behind Every Embedding (Ep.02.00)
Every embedding, similarity score, and attention weight in modern AI comes down to vector operations. Module 2 of From Zero to Agents builds vectors, norms, and dot products from first…
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The “Bank” Problem: Why Your RAG System Might Be Retrieving the Wrong Thing
A word embedding for “bank” has to mean both a riverbank and a financial institution at once. Here’s why that quiet compromise causes real retrieval errors in production RAG and…
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Why Your Chatbot Breaks on Typos and What Actually Fixes It
A single misspelled word can silently degrade an LLM application’s output. Here’s what’s actually happening under the hood, and why byte-level BPE tokenization — not better prompting — is the…
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Byte-Pair Encoding Explained: Why Tokenization Isn’t Just Splitting on Spaces (Ep.01.00)
Every episode of Module 00 quietly relied on .split() — text broken into whole words at whitespace, no questions asked. It was a deliberate simplification, flagged twice and never resolved.…
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Is Your AI Agent Actually Intelligent, or Just a Very Good Parrot?
Every AI agent demo looks intelligent. That’s the problem. A demo is, almost by definition, a curated set of inputs the builder already knows the system handles well. The interesting…
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What Is Intelligence, Really? (From Zero to Agents, Episode 00.01)
Last episode ended with a question. Here’s a definition worth pressure-testing (a very common first instinct — someone may say): “A system is intelligent if it can analyze requirements and…
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Recent Posts
- Vectors, Norms, and Dot Products Explained: The Math Behind Every Embedding (Ep.02.00)
- From Words to Attention: The Full Story of How AI Learned to Represent Language (Summary)
- The “Bank” Problem: Why Your RAG System Might Be Retrieving the Wrong Thing
- Why Your Chatbot Breaks on Typos and What Actually Fixes It
- From Static to Contextual Word Embeddings: The Idea Behind Attention (Ep.01.02)
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