15 posts found
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...
Design tokens are the interface AI needs
A token that only names a hex value has saved you nothing. A token that names a job tells a model which decision it is m...
Generating layouts that respect a grid
Generated layouts usually look almost right, and "almost" is where the eye catches. Most of the fix is stating the rules...
Why component names matter more with AI in the loop
A human reads a bad component name and opens the file. A model reads it and guesses. Naming quietly became an interface...
Reviewing AI-generated UI: a checklist
Generated interfaces fail in a predictable set of places. Knowing which ones turns review from a vague unease into a lis...
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...
Prompt caching: the cheapest speedup you are not using
If every request begins with the same two thousand tokens of instructions, you are paying full price to resend them. Ord...
Streaming responses without breaking your UI
Streaming makes a slow response feel fast. It also means rendering text that is syntactically incomplete, and handling a...
Rate limits, retries and the backoff you actually need
Every AI feature meets a 429 eventually. Whether that is a blip or an outage depends on retry logic written before you n...
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...