According to Google DeepMind, the implementation of the SAS prompt enables Gemini language models to systematically learn from a robot's operational history, allowing for detailed analysis of parameter effects and actionable suggestions for optimization. For algorithmic trading and AI-driven crypto strategies, this method enhances backtesting and real-time adjustment capabilities by providing precise data-driven feedback, similar to personalized coaching. This advancement can improve the efficiency of high-frequency trading bots and automated market makers by enabling smarter, adaptive parameter tuning based on historical performance data (Source: Google DeepMind, Twitter, April 30, 2025).