LLMs Explained Like System Design.
Start with foundational concepts— neural networks, tokens, embeddings, vectors, layers—and learn how they fit together without getting deep into the math. Tap to explore and learn at your own pace.
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FeaturedPeel back the layers of Large Language Models to understand the artificial neuron, the power of ReLU, and how these simple units power the massive Transformer architecture.
FeaturedWhat does the '7B' on an LLM really mean? This article provides a rigorous breakdown of the Transformer architecture, showing exactly where those billions of parameters come from and how they directly impact VRAM, latency, cost, and concurrency in real-world deployments.
FeaturedHow a simple idea — “predict the next thing” — powers everything from ChatGPT to image generators.
Deconstructing the Giants: A Technical Deep Dive into LLM Architecture, Performance, and Cost
What does the '7B' on an LLM really mean? This article provides a rigorous breakdown of the Transformer architecture, showing exactly where those billions of parameters come from and how they directly impact VRAM, latency, cost, and concurrency in real-world deployments.
From Classifier to Creator: The Generative Leap
How a simple idea — “predict the next thing” — powers everything from ChatGPT to image generators.
Deep dive into LLM Inference Engine
We've explored the intricate architecture of the Transformer model—the billions of parameters that form its brain. But a brain, no matter how powerful, is useless without a nervous system and a life-support machine. That system, in the world of AI, is the inference engine.
What is a Neural Network?
Learn what a neural network is and how it works conceptually. No hard math, just logic.
Understanding Embeddings: The Secret Language of Meaning in AI
Learn what embeddings are, how embedding models create them, how to store and query them efficiently, and what trade-offs to consider when scaling large RAG systems.
Beyond RAG: A Technical Deep Dive into Gemini's File Search Tool
Making Large Language Models (LLMs) reason over private, domain-specific, or real-time data is one of the most significant challenges in applied AI. The standard solution has been Retrieval-Augmented Generation (RAG), a powerful but often complex architecture. Now, Google's Gemini API introduces a File Search tool that promises to handle the entire RAG pipeline as a managed service. But does this new tool truly make traditional RAG pipelines obsolete?
