Is Your ‘RAG’ Leaving Your Knowledge Base Exposed?

Your kitchen sink is not today’s topic of discussion, but if your RAG isn’t in order, employees may get search results that include everything and the kitchen sink.

Outdated articles. Duplicate answers. Slightly different versions of the same policy living in different places.

And that’s not good.

Answers that expose the problem

RAG, or Retrieval-Augmented Generation, is what many AI search and assistant tools use today. Instead of AI answering questions purely from its training data, it first retrieves relevant documents from your knowledge base, then uses those documents to generate an answer.

AWS explains it simply: RAG grounds AI responses in your actual data so answers are more accurate and relevant.

Sounds great, right? But this also means that your content debt is now front and center.

AI doesn’t fix content. It repeats it - confidently

RAG systems don’t judge content quality. They don’t know what’s o”ld but still indexed” or “technically accurate but misleading.”

They just retrieve what’s there and build answers from it.

So if your knowledge base includes:

• outdated policies

• duplicate articles

• conflicting guidance

• half-retired “just in case” pages

…the AI will happily use them.

Research on RAG systems consistently shows that retrieval quality directly determines answer quality. If the retriever pulls the wrong or incomplete context, the generated answer will reflect that.

Why this hits ServiceNow especially hard

In platforms like ServiceNow, the knowledge base isn’t just documentation - it’s where employees go when something is wrong, confusing, or urgent.

When RAG-style AI search or copilots sit on top of a ServiceNow knowledge base:

• Employees don’t see “search results”

• They see answers

And when those answers are wrong, outdated, or inconsistent, employees don’t blame retrieval logic or taxonomy.

They blame the system.

That’s a big shift. Traditional search lets employees hedge: open multiple tabs, skim, cross-check. RAG removes that safety net by synthesizing one response. If your content disagrees with itself, the AI may blend conflicting sources into something that sounds right but isn’t.

This isn’t an AI problem

RAG is doing exactly what it’s supposed to do: use your content to provide better answers.

The problem is most enterprise knowledge bases weren’t built for this level of scrutiny. They were built to store information, not to act as the single source of truth for automated answers.

That’s why AI makes content debt impossible to ignore.

Not because AI is flawed, but because it removes the buffer that used to hide inconsistencies.

Here’s what to do

If AI-driven answers are part of your ServiceNow present or future, content has to be treated like infrastructure, not inventory.

This means:

• Clear ownership for every article

• Regular review and retirement cycles

• Fewer duplicates, more consolidation

• Metadata that helps retrieval, not just navigation

• Alignment across HR, IT, and intranet content

Without those basics, AI will just scale confusion faster.

The bottom line

Retrieval-Augmented Generation turns knowledge bases into the raw material for answers employees trust - or don’t.

If content is fragmented, outdated, or unclear, AI will make that obvious.

And once employees see it, they won’t unsee it.

——

If our perspective resonates with you, The Employee Content Experience Playbook goes deeper into how employees actually experience content and why most organizations misdiagnose the problem.

It’s designed to reframe thinking, not prescribe solutions.