Reliable Agentic RAG with LLM Trustworthiness Estimates (pub.towardsai.net)

This article demonstrates an agentic system to ensure reliable answers in Retrieval-Augmented Generation, while also ensuring that latency and compute costs do not exceed the processing needed to accurately respond to complex queries. Our system relies on trustworthiness scores for LLM outputs, in order to dynamically adjust retrieval strategies until sufficient context has been retrieved to generate a trustworthy RAG answer.