12 articles
The design job for an AI feature is to capture the model's value while making its uncertainty legible and its errors survivable. Stream for perceived speed, cite for trust, treat the unhappy path as the main work, and capture feedback to fuel the flywheel.
A production AI app is a pipeline of checkpoints around one model call: guard the input, pick the simplest orchestration that works, route through a gateway, guard the output, and always have a deterministic fallback. Here is the whole map.
The hard part of an AI product is not the code, it is the judgment about what "good" means. That needs a single owner, not a committee, and an incident response loop that ends in a new eval row.
Hallucination, injection, and drift are properties of the technology, not bugs you fix once. The senior move is to detect, contain, and monitor every one of them, and to keep a live risk register.
SaaS has near-zero marginal cost. An AI product pays real inference money on every request, so you can ship something users love that loses money per use. Here is how to model and control that.
An AI product silently degrades weeks after launch with no code change. LLMOps is the loop that keeps it good, and the data flywheel is how production usage compounds into a product a competitor cannot clone.
An AI feature will be wrong some fraction of the time, forever. So do not gate ship on "no bad outputs." Gate it on containment: narrow scope, a staged ramp with checkpoints, a deterministic fallback, and an instant undo.
The model is a third-party dependency that reprices, deprecates, and changes behaviour on a schedule you do not control. None of that is a bug in your code. Here is how to build so it does not surprise you.
LLMs fit language work that tolerates some imperfection and is expensive to do by hand. Here is a concrete test for whether your problem qualifies, and what to do when it fails the test.
Writing your evaluators up front, like TDD for prompts, backfires. Evals have to be discovered from real failures. Here is the discipline that actually works, and how small a real suite ends up being.
Model capability is a commodity you rent by the token. If a competitor's model gets twice as smart next quarter, do you die or thrive? Here are the moats that survive, and the ones that only feel like moats.
A working AI demo takes an afternoon. A product a business runs revenue through takes a quarter. The gap is not model quality; it is the 95% wrapped around the model. Here is what that 95% is.