2 posts tagged with "knowledge-base"

Your RAG system is probably indexing its own outputs. You just don't know it yet.

It starts innocuously: someone adds a quarterly summary document to the knowledge base. That summary was written by the same LLM that queries the knowledge base. Six months later, a developer adds AI-generated release notes. Then auto-generated support FAQs. Then a synthesized onboarding guide. None of these documents are labeled as AI-generated. To the retrieval system, they look identical to human-written primary sources. Now when your model retrieves context to answer a question, a significant portion of that context is the compressed, possibly-distorted output of a prior model run — and your accuracy metrics are still green.

This is provenance debt: the accumulation of AI-generated content in retrieval corpora without source markers, creating a feedback loop where each generation of model outputs becomes raw material for the next.

Here is an uncomfortable finding from Google Research: when a RAG system retrieves insufficient or outdated context, the hallucination rate doesn't stay flat — it jumps from 10.2% to 66.1%. Adding a stale knowledge base doesn't make your AI help center neutral. It makes it sixfold more likely to give a confident wrong answer than if you had shipped nothing at all.

Most teams building AI-powered search and help centers focus on retrieval quality, embedding models, and chunk size. Almost none of them have a process for tracking whether the documents in the corpus are still accurate. That gap — documentation debt — is now showing up as a production reliability problem, not just a content problem.