1. Define the decision, not just the task
“Summarize this document” describes model activity. It does not describe the user decision the summary must support, the information that cannot be lost, or the consequence of being wrong.
Production work begins by defining who uses the output, what they do next, what evidence they need, and when a person must review the result. Those answers change the interface, evaluation set, data flow, and operating controls.
2. Treat context as architecture
LLM applications often depend on knowledge that is private, permissioned, or changing. Retrieval is therefore not a generic add-on. Source ownership, document structure, freshness, chunking, metadata, access control, and citation behavior are product architecture.
If a user can retrieve information they should not see, or cannot tell which source shaped an answer, a fluent response does not make the system ready.
3. Build evaluation before scaling usage
A team cannot improve quality it has not defined. Representative tasks, expected behaviors, unacceptable failures, and review criteria should exist before a pilot expands.
Useful evaluation combines deterministic checks, model-based analysis where appropriate, and domain-expert review. The aim is not to produce one universal score. It is to create a release decision the product team can defend.
- Representative real-world inputs
- Explicit failure categories
- Quality thresholds by task
- Human review for consequential outputs
- Regression checks across model or prompt changes
4. Make permissions and failure visible
AI systems introduce uncertain outputs into otherwise deterministic products. The interface must show source, state, review responsibility, and what the system could not complete.
On the service side, timeouts, model availability, tool failure, malformed output, and policy rejection need deliberate behavior. A graceful stop with useful recovery is better than hidden partial work.
5. Operate model, cost, and behavior together
Traditional application telemetry is necessary but insufficient. Production AI also needs visibility into model and prompt versions, retrieval behavior, tool calls, evaluation results, latency, and cost.
The team should be able to answer what changed, which users were affected, whether quality moved, and how to return to a known state. That operating loop is what turns an experiment into a maintained product capability.