ai
LLM features that survive production
A demo is easy. A feature that stays good as your data, prompts, and models change needs an evaluation harness from day one.
Before you tune a prompt, write ten to twenty examples with expected outcomes. That's your harness. Run it on every change.
Retrieval beats fine-tuning for most product features. Keep the source of truth in a database you control, not in model weights.
Model providers change models under you. Your eval harness is how you find out before your users do.
The fastest way to de-risk a build is to get one real feature all the way to production before you commit to the architecture.
Kubernetes is a fine answer to problems you actually have. Here is how we decide whether a team has them yet.
Static mockups hide the hard parts of an interface: timing, state, and what happens when something goes wrong.
We bring the same scepticism and rigour to client work. Tell us what is stuck.
