3 posts tagged with "knowledge-base"

A support engineer pastes a customer ticket into your internal assistant. The question is sharp: "Does our API support multi-region writes on the free tier?" The assistant comes back instantly, citing a chunk it retrieved with 0.91 cosine similarity. The answer is yes. The chunk is from a landing page written by marketing in 2023 to win a head-to-head against a competitor. Engineering removed multi-region writes from the free tier eighteen months ago and posted a terse internal RFC that nobody linked from a customer-facing page. The RFC is also in the vector store. It scored 0.74.

The assistant didn't hallucinate. It retrieved the highest-scoring document and faithfully grounded its answer in the text. The retriever did its job. The job was wrong.

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Here is an uncomfortable finding from Google Research: when a RAG system retrieves insufficient or outdated context, the hallucination rate doesn't stay flat — it jumps from 10.2% to 66.1%.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.