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This article details how a systematic fund replaced its traditional NLP pipeline with a RAG-based LLM agent architecture, achieving a 340% improvement in alpha generation from unstructured data. It cites recent research (Alpha-GPT 2.0, FinCon, FinAgent) showing significant gains in automated factor discovery and trading performance.