RAG Isn’t Dead: Build a Knowledge Fabric that Actually Deflects Tickets
5 min read

RAG Isn’t Dead: Build a Knowledge Fabric that Actually Deflects Tickets

Plain RAG underperforms on messy enterprise content. The fix is upgraded retrieval: clean corpus, hybrid search, GraphRAG, and tenant-aware memory with citations by default.

Diligra - Founders

Reality check
“RAG is dead” hot takes ignore that retrieval has evolved: enterprises are adopting graph-aware retrieval and better vector practices because it grounds LLMs with sources and boosts deflection.

What’s working now

  • GraphRAG. Build a knowledge graph over your corpus; retrieve along relationships (Service → CIs → owners) to improve reasoning and grounding.
  • Fit-for-purpose vector memory. Vector DBs remain central to enterprise retrieval; modern RAG stacks (dense + keyword + filters) are table stakes in 2025.

Your knowledge fabric checklist

  1. Scope the corpus: KB, runbooks, solved tickets, architecture docs; strip rot, dedupe, add ownership metadata.
  2. Chunk + enrich: sensible sizes, titles, service/CI tags, severity, dates.
  3. Hybrid retrieval: semantic + keyword + filters (tenant, product, CI).
  4. Graph relationships: who owns what; which runbook fixes which class of incidents; dependencies between services and CIs.
  5. Answer + cite: always return snippets with links; never free-hallucinate.
  6. Close the loop: harvest solved tickets into draft KBs; thumbs up/down feeds retraining queues.

Measure
Deflection (portal + chat), first-touch resolution, time-to-first relevant article, article helpfulness ratio.

Where Diligra helps
Diligra ships tenant-aware memory and graph-ready retrieval so Virtual Agent and Agent Assist can respond with citations and our Knowledge Ghostwriter turns solved tickets into publish-ready articles.