99% of Developers Don't Get LLMs

Large language models work by predicting the next token in a sequence using transformer architecture with self-attention mechanisms. They're trained on massive text datasets to learn patterns, grammar, and relationships between concepts. The transformer processes all tokens simultaneously rather than sequentially, allowing better capture of long-range dependencies. Generation happens through probability distributions over vocabulary, with techniques like temperature and top-k sampling controlling randomness. Models become more capable with scale, exhibiting emergent behaviors not present in smaller versions. Raw models are aligned with human preferences through reinforcement learning with human feedback (RLHF). Despite their fluency, LLMs have significant limitations including hallucination, lack of persistent memory, and sensitivity to input phrasing.