🔍 Retrieval quality > model quality for most RAG apps
PM RAG Products
(2026 Edition)
5 stack layers and 4 pitfalls for RAG product PMs.
Build RAG PM Skills — Free →5 Stack Layers
1.
Document ingestion and chunking
2.
Embedding model and vector store
3.
Retrieval — semantic search + filters
4.
Reranking — improve top-k quality
5.
LLM synthesis with citations
4 Pitfalls
❌
Bad chunking destroys retrieval quality
❌
Skipping reranking — top-k is rarely best-k
❌
Hallucination from synthesis even when retrieval is correct
❌
No eval set — quality regressions go unnoticed
FAQ
Should every AI app use RAG?
No. RAG fits when answers must come from your data (docs, knowledge base, customer history). For tasks the base model already knows (general writing, code), RAG adds latency and complexity without quality lift. Use RAG where grounding matters; skip it where it doesn't.