A new retrieval-augmented generation (RAG) agent has been developed to address the common issue of LLMs confidently hallucinating answers. This agent, part of the Agentic AI from Zero project, is designed to provide inline citations for grounded answers, flag low-confidence responses when information is thin, and resort to a web search fallback when no relevant information is found in its knowledge base. The agent uses a combination of TF-IDF, cosine similarity, and the model's self-reported grounding to determine confidence levels, ensuring that answers are always supported by evidence or clearly marked as uncertain. AI
IMPACT This RAG agent's approach to confidence flagging and citation could improve the trustworthiness of LLM-generated answers in applications.
RANK_REASON The item describes a specific RAG agent implementation with novel features for confidence flagging and citation, which is a tool-level development.
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