What You'll Learn
How an LLM turns raw token scores into probabilities before choosing what comes next How temperature reshapes a probability distribution to make outputs more predictable or more varied How top-p sampling limits which tokens the model is allowed to consider Why top-p adapts to model confidence differently from a fixed top-k cutoff How temperature and top-p interact when both are applied to the same distribution How to choose sampling settings for factual, structured, professional, and creative tasks What You'll Learn
Why language models break text into tokens instead of reading whole words How subword tokenization balances vocabulary size with the amount of text a model must process How Byte Pair Encoding (BPE) learns useful token boundaries from patterns in training data Why the same sentence can use very different numbers of tokens across languages, code, and rare words How tokenization can cause surprising failures in spelling, letter counting, and unusual inputs Why token counts affect context limits, processing efficiency, and the cost of using an LLM What You'll Learn
Why an LLM’s context window is not the same thing as memory How chat applications create continuity even though the underlying model is stateless How attention lets earlier parts of a conversation influence the next token Why longer conversations become increasingly expensive for a transformer to process Why information can become harder to use even while it remains inside the context window How truncation, summarization, retrieval, and KV caching help manage long conversations
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