According to Andrej Karpathy, large language models should be treated as simulators that channel multiple perspectives rather than as entities with their own opinions, source: Andrej Karpathy on X. He advises replacing you centric questions with prompts that ask what different groups would say, which is directly applicable to structuring crypto market research, source: Andrej Karpathy on X. Applying this to trading workflows, practitioners can prompt simulated bulls, bears, and market makers to generate scenario narratives for BTC and ETH without assuming the model holds a personal view, source: Andrej Karpathy on X. He adds that forcing a you voice only makes the model adopt a personality implied by finetuning data statistics, reinforcing role based simulation as the correct mental model for AI assisted analysis, source: Andrej Karpathy on X.

According to @milesdeutscher, concise AI prompts can deliver a quick pulse of the entire crypto market in seconds and have been integrated into his daily morning routine, highlighting a fast decision-support workflow for traders focused on market timing and liquidity awareness, source: X post by @milesdeutscher, Aug 14, 2025. He demonstrated this by using Perplexity with a specific prompt to analyze altcoin volume rotation over the past 72 hours, emphasizing rapid identification of where trading volume is rotating across altcoins, source: X post by @milesdeutscher, Aug 14, 2025. The post underscores a practical method for traders to scan recent altcoin volume rotation to inform short-term positioning and momentum monitoring without manual data aggregation, source: X post by @milesdeutscher, Aug 14, 2025.