I ran a RAG index for 13 months. 89.73% is stale, orphaned or a duplicate.
Article summary
Quick briefing — cleaned from the original RSS feed
A reproducible teardown of what happens to a vector index after 13 months of real document churn, across pgvector, Qdrant and Chroma. 07 August 2026 · Rostyslav Myronenko Why I built this I build, consult and teach in the GenAI domain, and I'm an active AWS Community Builder in AI Engineering category. One of my friends came to me with some version of "Why retrieval quality of our in-house RAG degrades over time?" - and I didn't have a satisfying answer other than "let me look". The root cause…
1Key Takeaways
- A reproducible teardown of what happens to a vector index after 13 months of real document churn, across pgvector, Qdrant and Chroma.
- 07 August 2026 · Rostyslav Myronenko Why I built this I build, consult and teach in the GenAI domain, and I'm an active AWS Community Builder in AI Engineering category.
- One of my friends came to me with some version of "Why retrieval quality of our in-house RAG degrades over time?" - and I didn't have a satisfying answer other than "let me look".
2AIWedia Score
8.2/10
High relevance — worth your attention today
Based on source trust, recency, category impact, and story depth.
3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a reproducible teardown of what happens to a vector index after 13 months of real document churn, across pgvector, Qdrant and Chroma.
Explore related
Browse toolsCoding AI news
Explore curated coding ai tools on AIWedia — compare, rank, and launch from our directory.
Full story on DEV — ML
Read full articleHeadlines aggregated via RSS for discovery on AIWedia. Original content © DEV — ML. We link to the source and do not republish full articles.