Querying content stacks and generating dynamic content stacks in cloud storage environments

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for pairing a content management system with a large language model (LLM) to intelligently query groups of related content items. In particular, the disclosed systems can generate content stacks comprising contextually relevant content items. The disclosed systems further utilize an LLM to analyze the content items within the content stack and generate one or more dynamic stack objects (e.g., summaries, timelines, decision logs, or insight views) that synthesize the meaning of content items within a content stack. The disclosed systems can automatically update the dynamic stack objects based on modifications to the content stack. The disclosed systems further generate a content stack data container comprising the content stack and the one or more corresponding dynamic stack objects. The disclosed systems further provide a stack canvas that corresponds to the content stack data container for display to a client device.