LLM Fundamentals · TRACK 01/06
LLM Basics
The core ideas behind every AI model you'll ever call.
// THE TRACK
01 · START HERELarge Language ModelsUnderstand what a large language model actually is and why a giant text predictor can write code, answer questions, and hold a conversation.BEGINNERLLM InternalsSee the single mechanism — predicting the next token — that powers every answer an LLM has ever given you.BEGINNERGPUs & AI ComputeUnderstand why a model is really billions of multiplications, and why GPUs — and their scarce VRAM — set the price of AI.INTERMEDIATEScaling LawsUnderstand the empirical laws that told labs how big to build — and why more data, parameters, and compute keep paying off predictably.INTERMEDIATEInside ChatGPTChatGPT is a GPT language model, tuned to act like a helpful assistant, wrapped in a chat interface that predicts your reply one token at a time.BEGINNERBase vs Instruct vs ChatLearn why the same model ships in base, instruct, and chat flavors — and which one sits behind the API you're calling.BEGINNERLLM vs GenAI vs AGIUntangle the buzzwords: where LLMs end, where generative AI begins, and what AGI would actually mean.BEGINNERFoundation ModelsUnderstand what makes a model a 'foundation model', why one base model can power thousands of apps, and how it differs from task-specific models.BEGINNEREmergent AbilitiesSee why certain abilities seem to switch on only once a model is big enough, and why scientists argue about whether that's real or a measurement artifact.INTERMEDIATEParameters & WeightsFinally understand what '7B' or '70B parameters' means, what a weight actually is, and why bigger numbers don't always mean a better model.BEGINNER