1d ago · 10 min read · Embeddings Explained: How AI Turns Words Into Numbers That Actually Mean Something The surprisingly elegant math that lets computers understand that "dog" and "puppy" are related — and why this powers everything from ChatGPT to your Netflix recommend...
Join discussionApr 13 · 10 min read · Welcome to Module B3 — Retrieval, Really. Five posts about the boring-sounding, product-defining skill of getting the right context into your model at the right moment. Module B1 taught you how to call the function. Module B2 taught you how to write ...
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Apr 12 · 10 min read · Until recently, most AI systems were monolingual in a very specific sense: each system worked on one kind of input. Speech recognizers took audio and produced text. Image classifiers took pictures and produced labels. Translation systems took one lan...
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Apr 13 · 11 min read · You've chunked your documents well. You've embedded them with a frontier model. You've stored them in pgvector with a clean HNSW index. Your eval set passes. And then a user searches for ERR_BILL_4042 — your company's specific billing error code — an...
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Apr 13 · 11 min read · The single most common mistake I see in RAG projects is this: a team spends a month tuning their embedding model and another month picking a vector store, and their retrieval quality is still mediocre. They conclude "RAG is hard." They start looking ...
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Apr 12 · 11 min read · Here is the weirdest thing modern AI does, and nobody talks about it enough. If you ask an AI system about the word "king," somewhere deep in its guts, the word isn't stored as a word at all. It's stored as a list of numbers. A long list — maybe 768 ...
Join discussionApr 12 · 10 min read · You walk into a party where you don't know anyone. For the first ten minutes, it's just a blur of faces. But by the end of the first hour, without anyone explaining anything to you, your brain has quietly sorted the room. There's the loud cluster nea...
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Apr 10 · 13 min read · I Built a RAG App That Chats With Any PDF — Here's How TL;DR: I built DocMind — a multimodal RAG (Retrieval-Augmented Generation) app that lets you upload any PDF, image, or document and ask questions about it in plain English. No API keys needed — i...
Join discussionApr 8 · 16 min read · { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is RAG architecture in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "RAG (Retrieval-Aug...
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Apr 7 · 10 min read · LLM memory embedding is the technique of translating information into numerical vector representations, called embeddings. These vectors capture the semantic essence of data, allowing AI models to store, search, and retrieve knowledge based on meanin...
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