I ship AI systems end to end.From messy data to a deployed, evaluated product.

Retrieval is evaluated before the generator is allowed to speak.

Decision

Score retrieval first, then add generation. The retriever is the part a reviewer can measure without trusting a model's prose.

Tradeoff

A retrieval only gate slows the build and exposes weak chunks early. That is the point. A fluent answer hides a bad retriever.

Tool use with typed inputs, explicit failure paths, and a trace a reviewer can read.

Decision

Every tool call returns a typed object and every step is logged. A free form agent loop is harder to evaluate and harder to debug.

Tradeoff

Typed tools and explicit branches take longer to add. They make the failure surface smaller and the transcript readable.

A latency budget by stage, with a fallback for every timeout.

Decision

Set a per stage latency budget before writing prompts. A voice turn that sounds smart after two seconds still feels broken.

Tradeoff

Short model calls and narrow tools reduce quality on complex requests. The fallback path has to be good enough to carry the turn.

Image input is treated as a signal problem, not a classification demo.

Decision

A low confidence result returns a review flag instead of a confident label. The interface has to make uncertainty actionable.

Tradeoff

The flag reduces automation and adds work for a reviewer. It also prevents the system from pretending a noisy trace is a diagnosis.