The Asymmetric Efficiency Manifesto: Why Vector RAG is a Dead End for Corporate Memory

1. 1Build a deterministic graph to replace stochastic vectors
2. 2Run experiments to compare the performance of stochastic vectors and deterministic graphs
3. 3Configure your system to use the deterministic graph for corporate memory
4. 4Test the new system for improvements in memory benchmarks
5. 5Apply this approach to other areas of your machine learning pipeline

Machine learning engineers and researchers can benefit from this approach to improve their corporate memory benchmarks, while data scientists can apply this knowledge to optimize their data storage and retrieval systems.

💡 Deterministic graphs can outperform stochastic vectors in corporate memory benchmarks

💡 Ditch stochastic vectors for deterministic graphs to shatter SOTA memory benchmarks!