Building a Knowledge Base Your AI Agent Can Actually Search
What it actually takes for Editor's AI agent to search your own knowledge base instead of answering from general training data alone.
"Why doesn't my AI assistant know about my own documents" is one of the most common frustrations people run into with AI tools generally, and the reason is almost always the same: the model only knows what's actually been indexed and made searchable to it. Saying "we use RAG" doesn't tell you what that requires from you. Editor's auto-RAG module handles the retrieval side, but it can only search what's actually been indexed — so it's worth understanding what that step involves.
What indexing actually means here
Retrieval-augmented generation, in practice, means content gets processed and stored in a form the agent can search semantically — not just keyword matching, but matching by meaning, so a question phrased differently than your document's wording can still find the right passage. Editor's implementation of this lives in its auto-RAG module (frontend/apps/editor/src/modules/auto-rag/), which handles both the indexing pipeline (RAGManager.ts) and the retrieval backend (CloudflareRAG.ts).
The practical implication: content you want an agent to be able to answer questions about needs to actually go through this indexing path. A document sitting in a folder somewhere that was never indexed isn't invisible by accident — it's invisible because it was never processed into the searchable form the agent queries against.
Building a knowledge base worth searching
A few habits make the difference between a knowledge base an agent can genuinely use and one that technically exists but rarely surfaces the right answer:
Write for retrieval, not just for reading. A document a human skims easily because of formatting and context isn't automatically easy for a retrieval system to match against a specific question. Clear section headings and self-contained paragraphs — ones that make sense pulled out of context — retrieve better than a document that only makes sense read start to finish.
Keep it current. A knowledge base an agent searches is only as good as its freshest content. An outdated policy document indexed alongside its replacement creates a real risk: the agent might surface the wrong one, confidently, with no obvious signal to you that it did.
Scope it deliberately. Not everything needs to go into the searchable knowledge base — internal drafts, abandoned proposals, and one-off notes add noise that can get retrieved ahead of the content that actually answers a question.
Check the module's own documentation before you commit to a workflow
The auto-RAG module ships its own README (frontend/apps/editor/src/modules/auto-rag/README.md), which is the most direct source for exactly what content types are supported, what the indexing pipeline expects as input, and how retrieval is scoped. If you're setting up a knowledge base for a team to rely on, read that directly rather than assuming based on how another tool's RAG feature works — the specifics of what's indexed and how it's chunked vary enough between implementations that it's worth confirming rather than guessing.