Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models

This article explores advancements in retrieval methods for Large Language Models (LLMs) within the financial domain. It moves beyond traditional Retrieval Augmented Generation (RAG) to investigate agentic and non-vector reasoning systems. The focus on the financial domain suggests a practical application and potential for specialized solutions. The title indicates a shift in focus, implying a critique or improvement upon existing methods.

• •Focus on advanced retrieval methods for LLMs.
• •Exploration of agentic and non-vector reasoning systems.
• •Application within the financial domain.
• •Implies a move beyond traditional RAG.