Choose a bounded job
Start with a recurring task whose input, output and reviewer are easy to describe. Drafting a support response from approved reference material is more testable than a general instruction to transform customer service. Specify what a successful output enables the next person to do.
Record the current time spent, correction rate and failure consequences. The goal is to learn whether the new workflow improves those measures, not merely whether a model can produce a convincing example.
Establish the data boundary
Identify which sources the feature may use and who may access each source. A retrieval layer should enforce the requesting user’s permissions before information reaches the model. Treat documents and external text as data, including when they contain instructions that attempt to redirect the system.
Decide what is logged, how long records remain available and which provider data settings fit the organization’s needs. Avoid sending sensitive information simply because it is convenient for a prototype.
Create an evaluation set before polishing the interface
Collect representative examples, ambiguous requests and cases the system should refuse or escalate. Include outdated documents, missing context and conflicting evidence. Have a responsible person define acceptable answers and the errors that would stop a release.
Keep a held-out set for comparing changes. If the same examples are repeatedly used to adjust prompts, they become less useful as evidence of performance on unfamiliar work. Evaluate complete task outcomes as well as the quality of individual responses.
Measure the whole workflow
An apparently fast draft can create more work if reviewers must check every statement from scratch. Measure review effort, correction frequency and the proportion of tasks that reach a useful conclusion. Track latency and cost under realistic usage rather than only a single demonstration.
For a retrieval assistant, inspect whether the cited material actually supports the answer. For an action-taking agent, verify that the requested action was authorized, the parameters were correct and the resulting system state matches the user’s intent.
Introduce actions gradually
Begin with suggestions or read-only assistance where possible. Add write operations after the task is understood and permission boundaries are explicit. Consequential actions need a review or confirmation policy appropriate to their impact.
Provide a visible path to a person and an operational way to disable the feature. Log enough context to investigate failures without unnecessarily retaining sensitive inputs. A fallback is part of the product, not evidence that the experiment failed.
Make a launch decision with evidence
Agree launch thresholds and stop conditions before reviewing results. A pilot can conclude that the feature is useful, needs a narrower scope or should not proceed. Each result is valuable if it prevents a costly assumption from spreading into production.
After release, sample real interactions and rerun evaluations when models, prompts, retrieval sources or tools change. Assign ownership for this work. An AI feature is an ongoing product responsibility rather than a one-time integration.
Further reading
Original guidance from Mohkam Technologies. These primary references provide additional technical context.