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When AI Learns From Its Own Context — Self-Improving Language Models
We're all trying to find the perfect "prompt," but what happens when our instructions to an AI get too complex? New research shows they can suddenly fail or "collapse," losing all their knowledge. In this episode, we explore "Agentic Context Engineering," a new framework that avoids this. Instead of a static prompt, it builds an "evolving playbook" that allows the AI to learn from every single task, failure, and success.
Inspired by the work of Qizheng Zhang, Changran Hu, and colleagues, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2510.04618
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By Anlie Arnaudy, Daniel Herbera and Guillaume FournierWe're all trying to find the perfect "prompt," but what happens when our instructions to an AI get too complex? New research shows they can suddenly fail or "collapse," losing all their knowledge. In this episode, we explore "Agentic Context Engineering," a new framework that avoids this. Instead of a static prompt, it builds an "evolving playbook" that allows the AI to learn from every single task, failure, and success.
Inspired by the work of Qizheng Zhang, Changran Hu, and colleagues, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2510.04618