<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Embeddings on Hitesh Pattanayak</title><link>/tags/embeddings/</link><description>Recent content in Embeddings on Hitesh Pattanayak</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 29 Mar 2026 21:14:51 -0700</lastBuildDate><atom:link href="/tags/embeddings/index.xml" rel="self" type="application/rss+xml"/><item><title>Vector Embeddings &amp; Similarity: The Foundation of RAG</title><link>/posts/vector-embeddings-similarity-the-foundation-of-rag/</link><pubDate>Sun, 29 Mar 2026 21:14:51 -0700</pubDate><guid>/posts/vector-embeddings-similarity-the-foundation-of-rag/</guid><description>A practical deep-dive into vector embeddings and cosine similarity — the mathematical foundation that makes retrieval in RAG systems actually work.</description></item><item><title>Vector Databases, ANN, and Chunking: Storing Knowledge for Retrieval</title><link>/posts/vector-databases-ann-and-chunking-storing-knowledge-for-retrieval/</link><pubDate>Sun, 29 Mar 2026 21:11:47 -0700</pubDate><guid>/posts/vector-databases-ann-and-chunking-storing-knowledge-for-retrieval/</guid><description>A practical guide for software engineers covering how vector databases use Approximate Nearest Neighbor algorithms to search millions of embeddings efficiently, and how to chunk documents intelligently so your RAG pipeline actually retrieves useful, precise context.</description></item></channel></rss>