6. Enhancing Performance with Targeted Learning
Generative AI models can learn on the fly by using:
In-Context Learning – Provide the agent with a few high-quality examples of successful trades or well-structured derivative positions.
Retrieval-Based Learning – Let the agent query a data store of historical patterns, e.g., “What happened when a major stablecoin depegged last time?”
Fine-Tuning – Train the base LLM on large volumes of historical DeFi data or specialized derivative pricing scenarios.
This layering of knowledge helps the agent refine its decisions, leading to better predictions and more robust risk management.
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