Opening — Why this matters now RAG systems are having an identity crisis. On paper, retrieval-augmented generation is supposed to ground large language models in facts. In practice, when queries require multi-hop reasoning, most systems panic and start hoarding context like it’s a survival skill. Add more passages. Expand the window. Hope the model figures it out. ...
Opening — Why this matters now Personalization has long been the Achilles’ heel of large language models (LLMs). Despite their impressive fluency, they often behave like charming strangers—articulate, but impersonal. As AI assistants, tutors, and agents move toward the mainstream, the inability to instantly adapt to user preferences isn’t just inconvenient—it’s commercially limiting. Retraining is costly; prompt-tweaking is shallow. The question is: can a model become personal without being retrained? ...