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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FeaturedWhy shrinking your model is like compressing a JPEG—and how to do it without lobotomizing your AI.
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.
FeaturedWe'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.
FeaturedLearn what a neural network is and how it works conceptually. No hard math, just logic.
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?
Why GraphRAG is the Next Frontier in Generative AI (Part 1)
Understanding the need for GraphRAG and how it overcomes the limitations of traditional RAG systems.
Navigating the Era of Perfect AI Image Edits: How to Spot Fakes and Safeguard Against Misinformation
AI tools like Google’s Nano Banana make flawless photo edits accessible to anyone—but they also supercharge the spread of fake images. Here’s how to protect yourself with practical techniques, tools, and critical thinking.
