How to Build Incremental RAG Indexing in .NET: A Real-World Walkthrough
Incremental RAG indexing is what separates a demo knowledge base from one you can still trust six months later. The first ingestion run is easy: read every document, chunk it, embed it, write it to a
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Ahmet Özel
AI Engineer. Computer Vision, RAG and LLM agents.
"The trouble starts on day two" is exactly right, and deletions are the case most incremental indexers get wrong. An updated document at least produces a new embedding; a deleted one leaves its vectors in place answering questions forever, and nothing notices because nothing is looking for absence.
Testing the edit that shrinks the chunk count is a good sign you thought about it, since that is the same bug in miniature: chunks 5 through 8 need removing, not just 1 through 4 rewriting.
The other thing worth carrying into chunk metadata is a document version, so an answer can state how old its source is and a stale-content audit becomes a query rather than a project.