10 posts found
What a large language model actually predicts
A model does not look anything up and does not decide what is true. It estimates which token comes next. Almost everythi...
Tokens, not words: how a model reads your text
Models do not see characters or words. They see tokens — and once you know how text becomes tokens, several odd behaviou...
Why temperature changes the answer, not the knowledge
Turning temperature down does not make a model more accurate. It makes it more repeatable — and confusing the two is how...
Embeddings explained without the linear algebra
An embedding turns text into coordinates, and nearby coordinates mean related meaning. That single idea is what makes se...
Training, fine-tuning and prompting are three different tools
Teams reach for fine-tuning when they need context, and for prompting when they need behaviour. Knowing which problem ea...
How to evaluate an AI feature before you ship it
You cannot unit test "is this a good answer", but you can build a set of real cases with known-good outputs. An afternoo...
The cost model of an AI feature
Per-token pricing looks trivial until you multiply by retries, conversation history and the context you resend on every...
Writing prompts your team can maintain
Prompts are code with none of the tooling. Treat them like code anyway — version them, review them, test them, and expla...
What to tell users when the model is wrong
Your AI feature will be confidently wrong in front of a user. What the interface does in that moment decides whether the...
AI adoption without a rewrite
The best first AI feature is small, measurable and easy to switch off. Start where a wrong answer is cheap and the value...