The End of Prompt Engineering? Stanford’s Self-Improving AI Learned Clinical Reasoning on Its Own

Author(s): Marie Humbert-Droz, PhD

Originally published on Towards AI.

Stanford’s Agentic Context Engineering lets models reflect, learn, and build their own playbook. I tested it on clinical lab data — and watched it teach itself temporal reasoning.

As we saw in my last post, large language models are getting really good at extracting information from clinical notes. The only place their performance diverged was in labs and procedures — where local models lagged slightly behind the big cloud providers.

The article discusses Stanford’s framework called Agentic Context Engineering (ACE) which aims to improve the efficiency and effectiveness of prompt engineering by allowing language models to learn and adapt their reasoning strategies autonomously. Through testing on clinical lab data, the author explores the capabilities of ACE in accurately extracting lab information while teaching itself to refine its reasoning with each case processed. It highlights significant advancements over traditional prompt tuning by emphasizing the importance of a self-learning system that critiques its performance, ultimately streamlining the process of clinical data abstraction and content extraction.

Read the full blog for free on Medium.

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Published via Towards AI