RakuScan: Hybrid AI Architecture Ensures Reproducibility in Investment Analysis

This article presents a brilliant architectural solution to the common problems of hallucination and token costs in financial analysis by using a hybrid Python-plugin system. By restricting the Large Language Model (LLM) to the role of an interpreter for deterministic quantitative data, the author achieves high scalability and reproducibility. It is an excellent example of pragmatic Prompt Engineering and system design that leverages the strengths of both traditional code and Generative AI.