13 posts found
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...
The context window is a budget, not a memory
Bigger context windows did not give models memory. They gave you a larger envelope to fill on every single request — and...
Why models hallucinate, and what actually reduces it
Hallucination is not a glitch that a better model will one day remove. It is what generation does when it has nothing to...
System prompts: the instructions the user never sees
A system prompt sets the rules for everything that follows. Written well it is the cheapest quality improvement availabl...
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...
What an AI agent can actually do with a design system
Given a real design system, a model stops inventing interfaces and starts assembling them. The difference is not the mod...
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...