Define a useful, measurable service
A RAG assistant retrieves information from a corpus to provide context to a generative model. For an enterprise, the first question is practical: which task should it help with, for whom, and with what reliability requirements? Finding an approved procedure and writing an exploratory summary require different controls.
Mintera recommends starting with a clear scope: one team, one corpus, a few question types and a business owner. Define the expected result and the situations where the assistant should acknowledge that it lacks sufficient sources.
Organise sources and permissions
Inventory the documents: origin, owner, update date and confidentiality level. Duplicates, obsolete versions and documents without an owner should be addressed before they become reference answers. Also plan how to remove documents that are no longer valid.
Permissions must be applied during retrieval. Users should not receive information from documents they cannot access. Test several profiles, including after role changes and revoked permissions. Storage location alone does not establish where model processing occurs: review both.
Evaluate more than a demonstration
Build a representative question set with your teams. Include simple questions, ambiguous wording, topics missing from the corpus and requests involving restricted documents. For each answer, examine cited sources, accuracy, acknowledgement of limitations and response time.
Keep these reference cases to test changes to the corpus, model or index. Evaluation then becomes a decision tool: it shows what improves, what deteriorates and where human review is needed.
Plan operations from the first pilot
Before opening access, assign responsibility for updates, incidents and user support. Define an operating budget and useful monitoring information while limiting personal data in logs. Provide a way to return to the normal process if the assistant is unavailable.
Mintera AI Services supports scoping, data preparation, integration and deployment. The goal is to connect the assistant to your tools and infrastructure, with acceptance criteria agreed with your teams.