A How-To Guide to Building Fast, Cheap, and Accurate Retrieval

Building generative AI prototypes is straightforward, but scaling them for production requires balancing accuracy, speed, and cost, especially when dealing with large query volumes. The guide focuses on optimizing embedding-based retrieval, crucial for modern AI systems like search, RAG, and agentic applications. It details techniques such as asymmetric retrieval, vector quantization, and dimensionality reduction to enhance performance. Asymmetric retrieval uses different embedding models for queries and documents to reduce costs, vector quantization compresses vectors to lower memory usage and latency, and dimensionality reduction truncates embeddings to improve efficiency with minimal accuracy loss. The guide evaluates these techniques using the NFCorpus dataset, demonstrating significant improvements in query latency and cost savings while maintaining high accuracy. It emphasizes the importance of selecting optimizations based on specific application needs, considering trade-offs between latency, cost, and accuracy.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.