Connecting Notion or Confluence to an AI agent takes minutes. Your docs get chunked, embedded, indexed — standard RAG pipeline. Ask it a question about a paragraph and it nails it.
Then someone asks, "What's the SLA for Enterprise customers?" The answer is sitting right there in a table. Two rows, four columns. The AI hallucinates an answer anyway.
We're not alone. The RAG community has been circling this problem for a while. One Medium post nailed it: "Parsers strip table headers first... the pipeline isn't doing retrieval anymore — it's doing educated guessing over orphaned rows." Another put it bluntly: "RAG only becomes reliable when the system respects the form of the data instead of flattening everything into text."
We've been building knowledge base integrations at Runbear — Notion, Confluence, Google Drive, SharePoint — and we kept hitting this exact failure mode. The pipeline works great on prose. On tables, it quietly falls apart.
What tables look like to your LLM
Here's a typical Notion table after it comes through the API:
<table header-row="true">
<tr><td>Customer Type</td><td>Initial Response</td><td>Reopened Ticket</td></tr>
<tr><td>Enterprise</td><td>within 2 hours</td><td>within 2 hours</td></tr>
<tr><td>Standard</td><td>within 8 hours</td><td>within 8 hours</td></tr>
</table>Most preprocessing pipelines convert this to Markdown:
| Customer Type | Initial Response | Reopened Ticket |
| --- | --- | --- |
| Enterprise | within 2 hours | within 2 hours |
| Standard | within 8 hours | within 8 hours |Looks fine to you. But two things go wrong before the LLM ever sees it.
First, chunking splits the table. When you're cutting documents into 800-token chunks, a pricing page with a paragraph, a table, and another paragraph gets sliced right through the middle. The header row ends up in one chunk. The data rows end up in another. As Ragie documented: "A chunk may end in the middle of a column such that the subsequent chunk includes some of the table data, but without the table headers so contextual information is lost."
