Retrieval-Augmented Generation (RAG) for Product Data

Key Concepts

• 1RAG grounds the agent in your real catalog: it retrieves relevant product data and feeds it to the model so recommendations reference actual, in-stock SKUs, not plausible inventions
• 2The retrieval quality ceiling applies — RAG is only as good as what it fetches, so embedding quality, chunking, and filters determine the outcome
• 3What you embed matters: rich product text (attributes, use-cases, reviews) retrieves far better than bare titles, tying RAG quality back to catalog readiness
• 4Freshness is critical for commerce RAG: price and stock change constantly, so the retrieved data must be current, not a stale snapshot in the vector store
• 5Hybrid retrieval and re-ranking (from earlier lessons) apply directly; pure vector search alone often under-performs for exact-match commerce queries
• 6Grounding reduces hallucination but the model can still misuse retrieved data, so guardrails should constrain claims to what the retrieved context actually supports
• 7RAG architecture decisions (index freshness, top-k, re-ranker depth) are cost/latency/quality tradeoffs the PM must own, not purely engineering choices

Why It Matters for PMs

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