Here’s the production-grade way to think about it 👇 The Ingestion Illusion Embedding 10M PDFs upfront is pure waste Most documents are never queried Instead: → Fingerprint PDFs → dedupe 30–40% instantly → Chunk semantically, not by fixed tokens → Embed on access, not on arrival Payoff: 5 TB shrinks to ~3 TB. Embedding bill drops 60% The Vector Tax Vector search is expensive when it’s your first filter Cosine similarity shouldn’t touch cold data Instead: → Keyword + metadata filter first → Narrow to top 1–5% corpus → Run vectors only on survivors Payoff: P95 latency improves 4–6× The Retrieval Funnel One retriever is brittle at this scale Instead: → BM25 for recall → vectors for relevance → Rerank top 50, not top 5,000 → Cache query embeddings aggressively Payoff: Recall stays high. Cost stays flat. The Context Budget Trap More context ≠ better answers It’s noise inflation Instead: → Compress chunks with summaries → Enforce hard token caps → Track answer attribution coverage Payoff: Token usage drops 70%. Accuracy goes up. Reframe: RAG is a retrieval system, not an embedding project. 🔖 Save this for your next RAG system design interview 💬 Comment “RAG” if you are also building a real-world architecture ➕ Follow for production-grade system design, not toy demos

Summary

The video outlines efficient strategies for building a retrieval system, emphasizing cost reduction and improved accuracy through better data handling and filtering techniques.

Key Points

1. Embedding 10M PDFs upfront wastes resources.
2. Fingerprint PDFs to deduplicate 30-40% instantly.
3. Use keyword and metadata filters before vector search.
4. Rerank top 50 results instead of top 5,000.
5. Compress chunks with summaries to reduce noise.
6. RAG is a retrieval system, not just embedding.

Tags

Repurpose Ideas

• LinkedIn post: Key strategies for efficient RAG systems.
• Tweet: 3 ways to optimize vector search costs.
• Checklist: Steps to design a production-grade retrieval system.