15 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...
An agent is a loop, not a personality
Strip away the branding and an agent is a small loop: call the model, run the tool it asked for, feed the result back, r...
Tool calling is an API contract the model can read
A tool definition is documentation with a schema attached. Write it for a capable colleague who has never seen your syst...
Why agents fail on long tasks
An agent that handles five steps beautifully can fall apart at twenty. The reason is rarely reasoning — it is that the c...
Giving an agent memory without giving it amnesia
Memory is not a feature you enable. It is a set of decisions about what to keep, what to summarise and what to let go —...
When a workflow beats an agent
If you already know the steps, do not ask a model to rediscover them on every request. A fixed pipeline with model calls...
RAG in one page: retrieve, rank, answer
Retrieval-augmented generation is three steps and a lot of tuning. The architecture is simple; the quality lives almost...
Chunking is the part of RAG nobody tunes
Teams spend weeks on rerankers and leave chunking at the default. It is usually the other way round that pays — how you...