Retrieval-augmented generation vs. deterministic tax computation in multi-agent financial advisory: A 2x2 factorial experiment

TL;DR

A custom capital gains calculation engine and a retrieval-augmented generation (RAG) system were tested in a multi-agent financial advisory context. Results showed that the tax optimization engine significantly reduced tax savings, while RAG alone provided competent recommendations without explicit tools.

Key Takeaways

• Explore RAG systems for financial advisory applications.
• Evaluate the impact of tax optimization tools on portfolio performance.
• Consider the trade-offs of using complex computation engines.
• Utilize pre-trained models for competent financial recommendations.