Let’s walk through exactly how WQW_QWQ (the query weight matrix) is multiplied with the input embedding, using a small example with embedding size = 5. 🧠 Context: What is WQW_QWQ? In a Transformer, we...
If you’ve ever wondered how models like ChatGPT or GPT-4 “understand” context and predict the next word so accurately — the secret sauce is attention. In this post, we’ll break it down step-by-step, with...
🟦 1. What is a word embedding? Let’s say a model reads this sentence: “The cat sat on the mat.“ Each word like “cat”, “mat”, “sat” is turned into a vector — imagine this...
Large Language Models (LLMs) like OpenAI’s GPT, Google’s Gemini, or Meta’s LLaMA have become incredibly powerful at understanding and generating human-like text. But how do they actually work under the hood? What happens when...
The Model Context Protocol (MCP) allows language models to interface with tools and external systems in a structured, programmable way. If you’re building intelligent apps or agents that need dynamic tool invocation, MCP gives...
In this post, we dive into the mechanics of how a GPT-like transformer model predicts the next word in a sequence. We’ll walk through a simplified version with small vectors and matrices to understand...
Question: If a system has 100 million user-product interactions, and if each order is mapped toa vector of 768 dimensions, how much space will the system require to store all vectors?Answer: 100 million interactions...
Two core reasons why vector databases are fundamentally different from traditional relational databases. ✅ 1. Binary Search Doesn’t Scale to High Dimensions Traditional Indexing (Relational DBs): Problem in Vector Space: Hence, binary search, B-trees,...
🔷 What are Transformer Layers? Each layer in a Transformer is like a stage in a mental process — every stage helps a token (word) understand more about its meaning in the sentence. A...
Vector embeddings convert real-world content, like documents and images, into 1-D numericalrepresentations (arrays).These arrays have N values, representing N dimensions. They are called vectors and can becompared with each other efficiently.These vectors aren’t random...