Learning agents with Redis: Feedback-driven context engineering for robust stochastic grounding

The document introduces a system that combines Large Language Models (LLMs) with Redis-backed learning agents to improve question answering over complex structured datasets. The system addresses the inefficiencies and high costs of using LLMs alone by employing a multi-agent architecture that leverages semantic caching and a multi-tiered caching strategy, storing results and execution patterns in a Redis-backed store. By learning from user feedback and query errors, the system develops a nuanced understanding of business terminology and rules, allowing for more accurate and efficient query generation. This approach is validated through use cases in the finance and insurance sectors, demonstrating improved query accuracy and reduced latency. The architecture incorporates components such as summarization, filtering, guidance generation, and interpretation, all coordinated by an orchestrator, to create a feedback-driven loop for continuous learning and adaptation. The system's ability to learn from both individual and collective user interactions aligns with principles of meta-learning, offering a more cost-effective and scalable solution for structured data querying.