Comprehensive beginner's guide explaining 33 fundamental LLM concepts without mathematics. Covers core mechanics like tokens, embeddings, and parameters; training processes including pre-training and fine-tuning; interaction patterns through prompts and context windows; architectural extensions like RAG and agentic AI; model
Table of contents
What is Generative AI?What is an LLM?The Hidden Machinery: What Happens UndergroundHow LLMs Learn: How the Machinery is TrainedShaping Behavior: Turning Raw Knowledge Into a Helpful AssistantHow You Talk to Them: Interaction LayerRunning in Real Time: What Happens When You Hit EnterArchitectures and Extensions: Building Beyond the BasicsDifferent Flavors of Models: LLM Families and TradeoffsMeasuring Performance: How We Know If They’re Any GoodWhere They Fail (and How to Patch Them)ConclusionSort: