NotebookLM is a phenomenal tool to read. Its power lies in its ability to retrieve information only from the sources you provide; you’ll never have to hunt down the odd source again. NotebookLM makes sure you know exactly where the data is coming from – but it can become a problem if you provide the wrong information. Claude, on the other hand, is better at searching and analyzing data to ensure you have accurate data. I thought Claude would be a better choice overall, given NotebookLM’s limitations.
But I spent some time testing the two and was surprised by what I found. NotebookLM maintains itself very well in many cases. While conflicting sources could once be a problem, NotebookLM now offers a broader set of tools to help you resolve inconsistencies in your data.
NotebookLM noted the conflict, but that’s not the same as its resolution
Although it showed the inconsistency, it didn’t mention which one I could trust
I first tested NotebookLM and Claude by throwing basic conflicts at them to see how each handled it. The test was simple: a mountain where two sources agree on most details but disagree on its overall height. NotebookLM highlighted the difference, pointing out what each source said, but stating that none of it was “the truth”. When I added another source that agreed with another source, NotebookLM pointed out that while two of the three sources agree with the figure, one still hasn’t been “officially” answered.
When I pushed it for a single, verified answer, NotebookLM noted that it could not answer definitively and that “the sources themselves do not provide additional context to determine which measurement is more accurate—such as publication dates or official government designations.” If there was more data than a few lines of text, the model would have responded better.
Claude didn’t judge, but he told me why and what to do next
He suggested that more research is needed
I provided Claud with the same three sources, and he also provided more information about the decision, although he did not respond. He said the figure was correct because two of three “apparently independent sources” agreed, but also that the discrepancy was remarkable because all three sources agreed on other details. Finally, Claude suggested checking a fourth, more reliable source.
Claude tried to verify using the information provided. This suggested that it was probably not a case of mistaken identity, as all other details of the mount matched. As other details vary from source to source, Claude suggested that the answers could all be AI-generated or free-source, and that an official source would likely provide more reliable information.
With a more difficult test, NotebookLM decided to answer, but Claude refused
And in this case, giving up is the right thing to do
I tested the models again, this time with a set of fictitious internal records of a company’s marketing budget. Two of the sources agreed, one disagreed. When I sent the question to NotebookLM, it deduced that the answer was $420,000, citing a conflicting figure in the records, but a more recent communication confirmed the higher cost. It was a pretty strong position, especially given the potential impact of getting the answer wrong when hundreds of thousands of dollars were on the line.
Claude took a more nuanced approach. He said he couldn’t provide a reliable number without resolving the conflict, and offered a potential answer by explaining his methodology for arriving at that number. He concluded that there was nothing in the documents to support a definitive answer, but the authority of the memo’s issuers and its more recent history made it more likely that it was true. However, Claude also noted that it’s best to seek a definitive answer before moving forward with inconsistencies like these.
NotebookLM works from what you give it, but it’s searchable
And can integrate with Gemini as well
I like to provide NotebookLM with limited resources. When I’m researching something specific (and I only want information from a few reputable publications), this is a great tool. On the other hand, NotebookLM allows users to search for other resources on the web and then quickly add them to the notebook. You can also take a step further and Leave your NotebookLM notebook on Gemini let artificial intelligence search the contents of your notebook and find the contacts you missed.
They are not created equal
Claude is an AI that can search the web and draw conclusions, but it’s also quite handy for analyzing sources and extracting information. However, it does not always provide clear sources. NotebookLM is not designed to do the same thing as Claude and is built to work with a more limited dataset (hence the resource limitation). While NotebookLM is nothing to scoff at, the differences between the two models boil down to this: they’re not designed for the same thing.
Both tools are good, but I don’t choose one over the other
Whether I use NotebookLM or Claude will depend on what I need it for. I assumed that Claude would be the more powerful option, and in many ways it is, but power isn’t everything, and it’s not capable of searching as much data as NotebookLM without burning a ton of tokens. If I want creative, generative functionality, Claude is the way to go, but searching through sources to find information? NotebookLM takes the cake.
