According to XDA-Developers, users are fundamentally misunderstanding Google’s NotebookLM by treating it like ChatGPT, which leads to subpar and frustrating results. The core issue is that NotebookLM is built as a personal research assistant using retrieval-augmented generation (RAG) to surface context strictly from uploaded documents, not as a free-form conversational chatbot. It lacks ChatGPT’s built-in reasoning, personality styles, and broad training, meaning its responses are entirely grounded in the quality of the sources you provide. The article demonstrates that asking it for personal advice or critiques, like “Is this UX approach good?” yields robotic, unhelpful outputs compared to ChatGPT’s more fluid responses. To use it effectively, prompts must be specific instructions, such as “Extract all notes on onboarding friction and group them by theme,” rather than open-ended questions. Essentially, NotebookLM can’t think for you or fill in blanks—it’s a tool for organizing and summarizing information you already have.
The RAG Reality Check
Here’s the thing: the article nails a critical distinction that’s easy to miss. NotebookLM’s reliance on RAG is its superpower and its limitation. It’s not pulling answers from a vast, generalized knowledge base. It’s literally performing a super-charged “Ctrl+F” across your documents and then trying to formulate a coherent answer from just those snippets. That’s why responses feel flat and literal. If your notes are messy or incomplete, NotebookLM’s output will be too. It has no external frame of reference. So asking it “What should I focus on next?” is a recipe for disappointment—it can only reorganize your past notes, not project a future path. ChatGPT, for all its flaws, can at least *improvise* an answer based on patterns it’s seen across the entire internet. NotebookLM can’t. And that’s actually by design.
Prompting for Power, Not Palaver
So how do you get value from it? You have to shift your mindset from “chatting” to “commanding.” The article’s advice is spot-on: good prompts are instructions. “Summarize these pages” is weak. “Pull every reference to Project Alpha’s budget and list them chronologically with page numbers” is strong. You’re giving it a discrete, actionable task that plays to its strength—processing and reformatting *existing* data. Assigning it a persona, like “Act as a design mentor reviewing these notes,” can help *frame* the output, but it’s not giving the AI new knowledge. It’s just telling it what tone to use when summarizing your sources. Basically, you’re the project manager, and NotebookLM is your ultra-fast, literal-minded research intern. You get out what you guide it to do.
Where It Fits in Your Toolkit
This isn’t to say NotebookLM is worse than ChatGPT. It’s just different. Think of it as a specialized tool for a specific job. If you’re a student with 50 PDFs of research papers, a journalist with interview transcripts, or a product manager with reams of user feedback, NotebookLM can be a game-changer for synthesis. But you have to do the groundwork first. You need to have, and be somewhat familiar with, the source material. It won’t write your essay, but it can brilliantly organize your annotated bibliography. It won’t design your product, but it can extract every user complaint about the checkout process from 100 support tickets. The friction people feel? That’s the friction of using a precision instrument like a scalpel when you thought you were picking up a Swiss Army knife.
