Taking AI From Prototype to Production

Define a useful task before choosing a model

A promising AI prototype is not yet a production feature. Begin by identifying the user task, the cost of an incorrect answer, and how success will be measured. A narrow, well-defined workflow is easier to evaluate than a general assistant expected to handle every situation.

Decide which cases should be handled by the model, which need conventional software logic, and when the system should ask for clarification or hand the task to a person.

Ground answers in reliable information

For questions about an organization's own information, retrieval-augmented generation can provide relevant source material to a language model at response time. Documents are prepared, divided into useful passages, and indexed so that relevant context can be retrieved for a query.

Retrieval quality depends on the source material, passage boundaries, metadata, and search strategy. Test the retrieval step independently: a model cannot reliably cite information that was not found or was out of date.

• Keep document permissions attached to indexed content.
• Track when sources were imported and refresh them when they change.
• Show references where users need to verify an answer.
• Provide a clear fallback when the available evidence is insufficient.

Evaluate the whole feature, not just model output

Create a representative set of inputs and expected outcomes before launch. Evaluate correctness, relevance, refusal behavior, latency, and cost. Include ambiguous requests, edge cases, and attempts to make the system ignore its instructions.

A model's fluent response can still be wrong. Where the consequences matter, add deterministic checks, constrained outputs, human review, or a workflow that makes uncertainty visible.

Protect data and limit access

Review what information is sent to model and infrastructure providers, how long it is retained, and what settings or contractual terms apply. Avoid sending information the feature does not need. Keep credentials server-side, restrict access to source data, and apply the same authorization rules to retrieved content as to the original records.

Treat model output as untrusted input. Validate structured responses before using them in application logic, and encode or sanitize content before displaying it in contexts where markup or commands could be interpreted.

Monitor and improve after release

Track performance, failure rates, response time, and usage cost with privacy-aware telemetry. Give users a way to report unhelpful responses, and periodically review a suitable sample of outcomes against the evaluation set.

Model and data changes can affect behavior, so version prompts and evaluation results. Roll out changes gradually and keep a way to disable or fall back from the feature if quality or availability drops.