# Keeping the Index Fresh — Retrieval-Augmented Generation (RAG)

Source: https://www.geekswithgeeks.com/en/rag/p-fresh

> Update, delete and version documents without rebuilding everything.

## Stale answers are wrong answers

Documents change, so the index must too. Use **incremental updates**: detect changed files by content hash or modified time, re-chunk and re-embed only those, and **delete** the old chunks (track them by document ID). Handle **deletions** and **access changes** promptly, since a removed or restricted document must stop being retrievable. Store **version and effective dates** as metadata and prefer the latest valid version in filters. When you change the embedding model or chunking, build a **new index alongside the old**, compare on the golden set, then switch over.

## Fresh, safe, fast, smart

Keep the index current, protect data, control cost and consider more advanced patterns.

![Four concerns: freshness, security, performance, advanced patterns.](assets/figures/rag/section-7-map.svg) — Figure 7.1 — Freshness, security, performance and advanced patterns.

## Schedule a full rebuild too

Incremental updates can drift. A periodic full rebuild into a new index, verified against the golden set, resets accumulated errors.

**Quiz:** How should you roll out a new embedding model?

- [ ] Change nothing
- [ ] Mix old and new vectors in one index
- [ ] Delete the old index first
- [x] Build a new index alongside the old, compare, then switch

*Answer:* Build a new index alongside the old, compare, then switch. Vectors from different models are incomparable, and a side-by-side check avoids regressions.