Polymathic

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Microsoft research reveals new prompt engineering techniques that makes GPT-4 achieve previously impossible performance levels

Microsoft’s research study demonstrates the effectiveness of advanced prompting techniques such as Chain of Thought (CoT) reasoning, dynamic few-shot selection, self-generated chain of thought, and choice shuffle ensembling in causing a generalist AI (GPT-4) to outperform a specialist AI (Med-PaLM 2) that was specifically trained for a given domain. This research paper confirms insights that advanced generative AI users have discovered, and the Medprompt technique has the potential to be used to elicit high quality output in any knowledge area, eliminating the need to intensively train a model on specific domains.

Original article: Researchers Extend GPT-4 With New Prompting Method


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About Me

Visionary leader driving digital transformation across higher education and Fortune 500 companies. Pioneered AI integration at Emory University, including GenAI and AI agents, while spearheading faculty information systems and student entrepreneurship initiatives. Led crisis management during pandemic, transitioning 200+ courses online and revitalizing continuing education through AI-driven improvements. Designed, built, and launched the Emory Center for Innovation. Combines Ph.D. in Philosophy with deep tech expertise to navigate ethical implications of emerging technologies. International experience includes DAAD fellowship in Germany. Proven track record in thought leadership, workforce development, and driving profitability in diverse sectors.

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