RAG accuracy jumped from 10% to 60% when I added outcome scoring (roampal.ai)

A recent experiment has showcased a significant improvement in AI memory systems through the introduction of outcome scoring. Traditionally, AI systems equipped with memory capabilities often struggle to learn from past mistakes, as they retrieve similar conversations without distinguishing effective advice from ineffective suggestions. In a scenario where an AI fails to correct its repeated poor advice, the lack of feedback-based learning becomes evident. The new approach, termed outcome learning, promotes successful advice while demoting ineffective suggestions based on user feedback, resulting in a marked accuracy increase from 10% to 60%. This advancement is crucial for the AI/ML community as it highlights the importance of integrating learning mechanisms into memory systems. The method scores memories based on user interactions—what advice was given, user reactions, and the outcomes of those interactions. This approach outperforms conventional methods, where simply storing conversations and adding taggings fail to facilitate meaningful learning. As developers and researchers prioritize smart memory systems, the realization that a system that avoids repeating past mistakes can be far more beneficial than one that merely recalls information is a game changer. The introduction of outcome learning promises to refine AI assistance, making interactions more efficient and helpful.