Gemini + NotebookLM =

"AI is becoming deeply embedded in product design workflows: from research synthesis to ideation and strategy. But as we increasingly rely on AI for higher-stakes tasks, one problem becomes impossible to ignore: trust. How do you make sure the insights you're getting are grounded in real data, not subtle hallucinations? This is where the combination of Gemini + NotebookLM becomes especially powerful. Used together, they create a workflow that balances strong reasoning with source-grounded accuracy."

"In this article, I'll show how to connect NotebookLM with Gemini to create a more reliable AI workflow for product research. Why to use NotebookLM integration in Gemini? TL;DR: to reduce AI hallucinations. One of the most critical risks of using AI for high-stakes tasks is hallucination. For example, when you're doing user research analysis and upload all your key info to Gemini, then ask it to extract insights, there is still a risk that the model will skip important details or invent information. This can lead to unreliable outcomes."

AI is being embedded across product design workflows, covering research synthesis, ideation, and strategy. Increasing reliance on AI for higher-stakes tasks raises a critical trust problem: hallucination and invented details. Gemini offers strong reasoning capabilities but can produce insights that are not fully grounded in source data. NotebookLM restricts itself to the sources provided, producing outputs tied to verified inputs. Integrating NotebookLM with Gemini creates a workflow that combines robust reasoning with source-grounded accuracy, reducing hallucination risk and producing more reliable product research insights.

Read at Medium