Analysis
This article details a fascinating journey of automating skill management using AI, specifically addressing the challenges of LLM outputs. The author's approach of iteratively refining the system, drawing inspiration from diverse fields like wine tasting and immunology, is a testament to the power of interdisciplinary thinking in AI development.
Key Takeaways
- •The project aimed to automate the skill inventory process for a collection of AI skills, tackling issues like outdated APIs and code duplication.
- •The initial design used skill origins to guide quality assessment but shifted to blind quality evaluation inspired by wine tasting.
- •The project revealed challenges in relying on AI's consistent output, requiring multiple iterations and a focus on deterministic code.
Reference / Citation
View Original"The article details the entire process: the design philosophy's trial and error, the implementation that discovered the "boundary between AI and decisive code," the bugs and security holes that the script revealed, and the reality seen by publishing in the ecosystem."