An AI assistant cannot answer consistently when internal knowledge is contradictory, outdated or scattered across files without owners. Before selecting a model, the team must decide which information is authoritative and how it is updated.
Define the knowledge scope
Start with one task, such as product questions, onboarding or internal procedures. Record which questions the collection covers and which should reach a person. This boundary is part of the product: it prevents confident answers on topics without approved material.
Select an authoritative source for each topic
If prices differ between a website, PDF and sales notes, the assistant cannot resolve the conflict independently. Define one source of truth per category and an owner who approves changes. Other documents should refer to that source or be labelled historical.
Give every page an owner and review date
Each knowledge article needs a title describing the question, a clear answer, conditions, exceptions, an owner and a review date. The date does not prove accuracy; it records when someone undertook to check the information.
Write passages that preserve their meaning
Separate topics, avoid ambiguous pronouns and place critical conditions beside the answer. Do not hide restrictions in footnotes. A passage read without the rest of the document should retain its essential meaning.
Test real and challenging questions
Build an evaluation set containing frequent questions, language variations, incomplete information and out-of-scope requests. Assess accuracy, references to the correct material and refusal where no basis exists. Record documents used and those requiring correction.
Turn failures into editorial work
When the assistant fails, do not immediately change only the prompt. Examine whether the cause is missing knowledge, contradiction, poor structure, incorrect retrieval or weak answering rules. Assign the appropriate correction and repeat the same test.
This knowledge-ownership and review framework is DigitalNow's original methodology.