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 types and deployment options; performance measurement through benchmarks and metrics; and common failure modes like hallucination and bias with their mitigation strategies. Emphasizes practical understanding over technical depth to help readers use LLMs effectively and recognize their limitations.
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: