In Part 1 we built a RAG pipeline that retrieves the right chunk almost every time. It's correct — and completely unshippable: the exact pipeline that answers perfectly in your notebook answers in 4 seconds and bills you $12,000/month in vector-database memory the moment real traffic hits.
This is the other half of production RAG: taking that same correct pipeline and making it FAST and CHEAP at 10 million documents. Same diagram, every node, now optimized for latency, dollars, and memory.
Four levers, held to the same three questions as Part 1 (what breaks without it, what it costs, what you're trading):
• COMPRESS — scalar/int8 (4× memory, ~30% faster, 99.99% quality), binary quantization (32× memory, up to 40× faster, oversample+rescore recovers 98–99.7% recall), product quantization, Matryoshka dims
• INDEX — the recall/latency/memory triangle (HNSW M & ef_search, IVF nprobe), and GPU indexing with NVIDIA cuVS/CAGRA (~12× indexing throughput)
• CACHE — prompt caching (−90% cost, −85% latency), semantic caching (GPTCache −68.8% API calls) and its false-hit trap
• SCALE — batching, concurrency, the reranker bottleneck, metadata pre-filtering, sharding & replicas
Then the loop that ties it together: measure P99, trace the pipeline, fix the one stage eating your budget, repeat — and know when to stop.
▶ Watch Part 1 first — "RAG: Beginner to Advanced" — it builds the pipeline this video optimizes.
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CHAPTERS
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0:00 Correct, but unshippable
0:39 The four levers
1:27 Trace the latency budget
2:14 Quantization — the memory wall
2:56 Scalar / int8
3:34 Binary quantization
4:41 PQ & Matryoshka dims
5:39 The index triangle (HNSW/IVF)
6:42 GPU indexing (cuVS/CAGRA)
7:35 Caching intro
7:51 Prompt caching
8:40 Semantic caching
9:36 Batching & concurrency
10:25 Sharding & partitioning
11:14 The cost math
12:05 The optimization loop
UPI : cybercreed@ibl
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