11 July 2026 · 4 min read
Qualify the use case first
The best AI features replace work a human is currently doing by hand: drafting, extracting, summarising, categorising. The worst ones add a chat box to a product nobody wanted to chat with.
If a deterministic rule or a search index solves it, use that. It is cheaper, faster, and debuggable.
Build the evaluation before the feature
Assemble fifty to two hundred real examples with known-good outputs before writing a prompt. Without this, every change is a vibe check and quality drifts invisibly.
Run the evaluation on every prompt and model change in CI. This is the single highest-leverage practice in shipping LLM features.
Ship behind a flag with a fallback
Roll out to a small cohort, measure task completion rather than engagement, and keep the non-AI path working. Model providers change behaviour without notice.
Log inputs and outputs from day one, with consent handled. You cannot improve what you did not record.
Where to go from here
If you are weighing an AI feature for an upcoming project, the useful next step is usually a short scoping conversation rather than a longer article. Bring your constraints — budget, timeline, existing systems, and the team you already have — and the decision usually resolves itself in an hour.
We run these as a fixed-scope discovery session with a written recommendation at the end, including the cases where our advice is that you do not need us.
Working on something like this?
Send us the brief. We will tell you what we would build, what we would cut, and roughly what it costs.
Talk to us