什么是 Towards Data Science？

RAG systems often incur hidden costs due to context over-fetching, lack of caching, and no model routing; the author built a cost control layer using semantic caching (98.5% hit rate), query routing (81% requests shifted to low-cost models), and token-budget circuit breaking, achieving 85.8% cost reduction at 10k requests/day without quality loss.

Proxy-Pointer RAG reduces the computational cost of entity and relationship reconciliation in knowledge graphs by over 90% by preserving document structure, enabling millisecond-scale ingestion without full-graph traversal.

The core reason gradient descent evolved into stochastic gradient descent (SGD) is computational scalability: as dataset size grows, batch gradient descent (BGD) becomes prohibitively expensive, while SGD updates parameters using only one or a few samples per iteration—reducing cost and leveraging noise to escape local minima; the article illustrates this via linear regression, deriving the closed-form solution from MSE and naturally motivating iterative optimization.