A document set that's grown over time often contains the same content uploaded multiple times with small edits — three versions of the same policy, two copies of the same manual from different folders. These near-duplicates crowd retrieval results and confuse which version is authoritative.
Detecting near-duplicates
- Compare document-level embeddings for high similarity (not just exact-text hashing, which misses reworded duplicates).
- Flag documents with near-identical titles or matching metadata (same source, close dates) for manual review.
- Check retrieval results periodically for the same underlying fact appearing from multiple near-duplicate sources — that's a strong signal deduplication is needed.
Handling them
Keep the most recent or most authoritative version, and either remove or explicitly mark older duplicates as superseded rather than silently leaving them competing for retrieval slots.
See metadata filtering in RAG pipelines for the related date/source filtering this builds on.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
