Edge of the Present: Pursuing AI’s Frontier in the Enterprise
summary
An enterprise team builds Mobius, an evolutionary prompting system that mutates prompts with LLMs, evaluates each generation against clear product and compliance signals, and recombines winners. A production use case runs 24 hours of continuous inference (≈3 million tokens) and climbs from 62% to 99% for ~$50 on a product-defined composite score. The approach tackles business‑critical constraints like availability and governance by automated verification loops rather than ad hoc prompt editing, and the harness engineering proves nontrivial.
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chapters (8)
- 0:00Introduction & Vanguard Context
- 1:09Emerging Technology Research Mission
- 2:29AI Progress Goes Vertical
- 3:39Business-Critical AI Constraints
- 6:00The Pink Elephant Problem
- 7:00Building Mobius: Evolutionary Prompting
- 8:27Results: 62% to 99% for ~$50
- 10:13Takeaways & Working Hypotheses
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