Internal Knowledge Assistants That Show Where an Answer Comes From[1]
Help your team navigate approved SOPs, policies and operational knowledge.
MTI Tech's proposed RAG and knowledge-assistant service focuses on a defined collection of business information. The assistant should help users locate relevant material and understand the basis of an answer while respecting source permissions and document ownership.
Refund requests over the set limit need a manager's approval.[1]
Retrieve first, then answer from what was found.
Retrieval-augmented generation, or RAG, combines information retrieval with generated answers. The system looks for relevant material in an approved collection and uses that material to support a response. It can improve access to business knowledge, but retrieval and generation can still make mistakes.
Source citations are useful for review, not a guarantee of correctness.
A manager approves refunds above the set limit.[1] Requests are logged in the support queue.[2]
One owned shelf, not every folder.
Begin with a collection that has a named owner and a process for updates.
Each with a named owner and an update process, like a controlled product knowledge set.
A large folder of contradictory documents is not automatically a usable knowledge base.
Five decisions behind every trustworthy answer.
Sources and ownership
Decide which documents are authoritative and who can update them.
Access
Ensure users can retrieve only the information their role permits. A public answer interface must not expose private material through its retrieval layer.
- Refund procedure
- Escalation contacts
- Approval limits by manager Manager only
Freshness
Track document versions and remove or supersede obsolete content deliberately.
Evidence
Connect responses with the relevant source passages so a user can inspect the basis of the answer.
Manager approval is required above the limit.[1]
“…requests above the set limit require approval from a manager before…”
Fallback
Define when the assistant should acknowledge insufficient evidence and direct the user to the responsible person.
I couldn't find enough approved material to answer this. The responsible person is the policy owner in HR Operations.
Score each part separately.
Build a reviewed set of normal, ambiguous and unanswerable questions. Include questions whose answer depends on access level and questions where a policy recently changed. Evaluate retrieval, answer support, citations and refusal or escalation separately.
Do not advertise a system as hallucination-free. The purpose of evaluation is to identify limitations and control the deployment boundary.
A catalogue someone keeps current.
Expansion to additional departments should follow a new permissions and evidence review.
Before you index anything.
Not necessarily. The appropriate architecture depends on the information, task and requirements. A retrieval approach should be compared with simpler search and existing tools.
That is not a safe default. Start with approved sources and defined users.
Source priority and version rules should be explicit. Unresolved conflicts should be surfaced rather than silently combined into a confident answer.
This service focuses on internal sources, permissions and knowledge maintenance. A public support assistant has a different audience and risk boundary. See AI chatbot development.
One team. One approved shelf.
Describe the recurring questions, current document sources and access requirements. That is the basis for a meaningful pilot.