Embeddings and rerankers at scale
How embedding and reranking models work, bi-encoders versus cross-encoders, a runnable similarity search with int8 compression (98% recall), memory maths for 100M vectors, approximate indexes, batching and throughput planning, and evaluation with recall@k.
16 min·Verified 2026-11Pro+50 XP
Retrieval-augmented generation, semantic search, deduplication and clustering all rest on embeddings: vectors that place similar things close together. At scale, the engineering is storage, indexing and throughput.
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