The hype around RAG is dead—unless you nail the nitty-gritty optimizations. Stephen Batifol cuts through the buzz to show how embedding model choice, vector indexes (FLAT vs IVF vs HNSW), and a rock-solid inference pipeline (batching, tensor parallelism, quantization, paged KV caches) make or break your self-hosted multimodal stack (Pixtral + Milvus + vLLM). He also debunks the “long context LLM” myth with Llama 4 vs Gemini 2.5 benchmarks, champions hybrid BM25+similarity search, and stresses proper evals over “vibe coding.”
By the end you’ll understand the speed vs accuracy vs cost trade-off matrix, why you need a hybrid search + metadata filters, and how to scale inference latency/throughput. Ready to stop guessing and start optimizing? Share your biggest indexing or embedding pain point!
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