Embedding Optimisation — Reduce Size Without Quality Loss

I will optimise your embedding dimensionality — using PCA, truncation for Matryoshka embedding models, or product quantisation to reduce embedding size, with quality evaluation confirming the dimensionality reduction doesn't degrade retrieval precision below your acceptance threshold. Embedding dimensionality directly impacts vector database storage cost, query latency, and memory footprint — a 3072-dimension OpenAI embedding costs 6x more to store and query than a 512-dimension equivalent; dimensionality reduction that preserves 98% of retrieval quality at 50% of the dimension count reduces infrastructure costs proportionally without meaningful quality impact.

The optimisation covers dimensionality reduction technique selection for your model and use case, PCA or truncation implementation, quality evaluation at multiple target dimensions, precision vs. cost trade-off analysis, and a recommendation for the optimal dimension count.