I've been conducting a systematic evaluation of NotebookLM for my revenue operations workflows, specifically for digesting sales enablement materials, competitive intelligence reports, and quarterly business reviews, which are frequently distributed as PDFs. My primary use case involves extracting structured data—particularly comparison tables, pricing matrices, and performance metrics—to feed into our CRM and forecasting models. Unfortunately, I've encountered significant and consistent issues with PDF parsing, especially concerning tabular data.
The core problem is that tables within uploaded PDFs are not being interpreted as coherent structures. Instead, the output is often a garbled stream of text where row and column relationships are entirely lost. For example:
* A competitor feature comparison table, with clear columns for "Product," "Enterprise Price," and "User Limit," is parsed as a single paragraph of jumbled text.
* Financial data from a quarterly report, where numbers are detached from their corresponding line items (e.g., "Q3 Revenue" value appears several lines away from its label).
* Multi-page tables are handled particularly poorly, with no continuity between pages.
I have attempted to mitigate this with several approaches, none of which have yielded reliable results:
1. Using different PDF sources (native digital PDFs vs. scanned documents).
2. Experimenting with the chunking method during source addition, though the control here is limited.
3. Providing explicit instructions to the AI regarding the table format after upload, which sometimes helps it reconstruct data from the corrupted parse, but is an inefficient and manual workaround.
This presents a major obstacle for anyone looking to use NotebookLM for analytical purposes. The inability to cleanly ingest one of the most common formats for business documentation severely limits its utility in sales operations and analytics contexts. I am curious if others in the community have experienced similar challenges.
Specifically, I would be interested to know:
* Whether this is a universal issue or if certain table structures (e.g., simple vs. complex, bordered vs. borderless) fare better.
* If any pre-processing of PDFs (using external tools) has proven effective before uploading them to NotebookLM.
* Whether Google's other AI offerings (like the PaLM API or Gemini in Vertex AI) handle the same PDFs with higher fidelity, which might indicate if this is a core model limitation or a NotebookLM-specific implementation issue.
A reproducible analysis of this bug would greatly benefit from users sharing their methodology and document types. For my part, I am compiling a set of sample PDFs with various table formats to document the exact failure modes.
Method over hype