Posts

The future of AI isn’t just about building better individual models — it’s about building better systems around those models. A raw language model, no matter how capable, is limited by what it learned during training and by what it can do in a single forward pass. Real-world AI products solve this by combining models with retrieval, memory, tools, and feedback loops. This post walks through four of the most important system-level patterns in production AI today: Retrieval-Augmented Generation, vector databases, AI agents, and diffusion models. ...

Every AI tool people use today — from ChatGPT to Claude and Gemini — is built on a series of breakthroughs that unfolded over several decades of research. It’s tempting to jump straight into Large Language Models without understanding the building blocks underneath them, but doing so often leaves a gap in intuition that makes everything else harder to follow. Once you understand Neural Networks, Transfer Learning, Tokenization, Embeddings, Attention, and Transformers, modern AI becomes significantly easier to reason about — not as magic, but as a series of well-understood engineering ideas stacked on top of each other. ...

Training a large language model is only the beginning of the story. After a model learns language from billions of examples during pretraining, engineers still need to make it more useful, safer, cheaper to run, and specialized for real-world applications. This is where a handful of complementary techniques come in: Fine-Tuning, RLHF, LoRA, and Quantization. Each solves a different part of the problem — specialization, alignment, training cost, and deployment cost. Let’s walk through each one. ...

Large Language Models are among the most important breakthroughs in modern technology. They power the chat assistants, coding tools, and search experiences that millions of people use every day. Yet most people who use ChatGPT or similar tools have never seen what happens between typing a question and getting an answer. This post walks through the core ideas behind LLMs in plain language — what they actually are, how they decide what to say next, and why they sometimes get things wrong. ...