Role
Process
Output
There was a need to gather initial information and quickly understand context to identify key problem areas, especially for team members who weren’t involved in the initial exploration of the topic.
The goal is to understand the history of an initiative and narrow down problem areas that could spark discussion in workshops with stakeholders from Kemendikdasmen and cross-functional teams (HoP, PM, Data, OPS).
Challenge: With several team members not involved in early research phases, the team needed to align their understanding and prepare contextual materials to refresh memory and stimulate productive workshop discussions.
The context was complex, even with available documentation. Speed and clarity were needed to ensure effective insight delivery across teams.
The goal is to support quick context absorption and design sustainable, accessible methods for teams to catch up on topics.
Challenge: During the catch-up phase, the team was often asked to provide insights to others who had neither the time to read full documentation nor a clear starting point.
NotebookLM was used to gather and synthesize insights from various research and product development documents. The tool sped up issue clustering around specific initiatives and offered versatile outputs tailored to different learning styles, from short audio podcasts to mindmaps and conventional notes.
In the context of compiling and distributing insights, the tool was also recommended to a new team member who used it to synthesize a topic they were learning, while exploring output formats that best suited their needs.
Preparation (Pre-AI Phase)
Using NotebookLM: Gathering & Unpacking Information
Using NotebookLM: Review & Output
Post-Output
But...
Verdict: NotebookLM can be a powerful onboarding tool for new team members. Topics or features can be broken down into more digestible sub-parts and synthesized efficiently.