Learning brief
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TL;DR
Large Language Models predict the next token (word fragment) in a sequence, trained on massive text datasets. They use a neural network architecture called the Transformer, which excels at understanding relationships between words. Despite the simple mechanism, this token prediction produces remarkably intelligent-seeming behavior.
What Happened
LLMs start with the Transformer architecture, introduced by Google in 2017. Transformers use 'attention' — a mechanism that lets the model weigh the importance of every word relative to every other word in the input. This is what lets an LLM understand that 'it' in 'The cat sat on the mat because it was tired' refers to the cat.
Training happens in two phases. Pre-training exposes the model to trillions of tokens of text, teaching it to predict the next token. This gives the model general language understanding and world knowledge. Fine-tuning (usually with RLHF) then aligns the model to be helpful and follow instructions.
Tokens are the fundamental unit — not quite words, not quite characters. 'Understanding' might be one token, while 'antidisestablishmentarianism' would be several. Models have a context window (the maximum tokens they can process at once), which ranges from 4K to over 1M tokens in current models.
So What?
Understanding the basics helps you use LLMs more effectively. Knowing that they predict tokens explains why they sometimes 'hallucinate' (they generate statistically plausible but false text). Knowing about context windows explains why they can lose track of information in very long conversations.
The training data cutoff also matters — models don't know about events after their training ended. This is why RAG and tool use are so important for production applications.
Now What?
Think in tokens, not words — it affects pricing, speed, and context limits
Use the largest context window you can afford for tasks requiring lots of input
Remember that LLMs are probabilistic — the same prompt can produce different outputs
Don't treat LLMs as databases of facts — treat them as reasoning engines that need good input