01
The demo proves possibility, not usefulness
A prototype usually answers one narrow question: can a model produce an impressive output from a curated input? A production system has to answer a harder set of questions. Who owns the decision? Which data can it access? How is quality measured? What happens when a provider is slow, a document is malformed, or the answer is confidently wrong?
This is why model selection is rarely the most consequential early decision. The durable work sits around the model: identity, data boundaries, evaluation, workflow integration, observability and recovery. If those boundaries remain implicit, the prototype has not reduced the real product risk.
02
Start with a verifiable decision
The strongest first use case is repeated, costly enough to matter, and narrow enough that a capable person can judge the output. ‘Review this supplier contract against these clauses’ is a system candidate. ‘Use AI for legal work’ is not. The first has inputs, a decision, an owner and possible acceptance criteria.
Verification changes the architecture. It may require citations back to a source document, structured output rather than prose, confidence thresholds, or a review interface that shows the model’s work in context. Those are product requirements, not late-stage polish.
- One defined input and one useful decision
- A human or rule that can judge the output
- A reversible action or explicit approval step
- A baseline cost, delay or quality measure
03
Evaluation belongs inside delivery
A handful of successful prompts is not an evaluation. Build a small representative dataset that includes common inputs, awkward edge cases and known failure modes. Record what ‘good’ means before tuning the system. The evaluation should survive changes in prompt, model, retrieval strategy and data.
In operation, capture the evidence needed to understand drift: input class, retrieved sources, tool calls, latency, cost, reviewer corrections and final outcome. Observability is useful only when it helps someone decide whether the system is improving or quietly creating work elsewhere.
04
Ship the smallest complete loop
A good pilot is production-shaped but deliberately small. It authenticates a real user, reads permitted data, performs one workflow, exposes its reasoning evidence, handles failure and records an outcome. It does not need to become a general platform before it proves value.
The objective is not to remove humans as quickly as possible. It is to create a controlled feedback loop where the system earns more responsibility through evidence. That path produces fewer spectacular demos, but far more software that teams keep using.