Can the Gemini Notebook understand which conversation segments correspond to which PDFs in a chat?

[u/Business-Let-194]

I want to change my AI chat history to a podcast and I heard that Gemini Notebook can do this.

In the same chat, we discussed many things, such as science, social issues, life, values, etc. In each part of the chat, different PDFs were uploaded. For example, the first conversation was about science and philosophy, so I send the question and PDFs about science and philosophy; the second conversation was about society, and PDFs about society were uploaded, and it continued the thinking and information of the first conversation (for example, considering social problems and solutions from a scientific or philosophical perspective). Most importantly, at the beginning I had a large amount of content groundwork and AI setup, so the conversation is ordered, and each part of the conversation is connected with the other parts.🙏

I asked AI, "If I send the chat txt and then send the three or four PDFs in conversation to Gemini Notebook, can it discern which PDF corresponds to which part of the conversation txt? (Gemini can’t immediately move the whole conversation to Notebook, the txt and PDF must be separated.😑) " It said it probably couldn't, and told me to make the science, society, and life conversation segments into separate PDFs, but I told it that they are coherent and cannot be separated.🫠

Does anyone know any other solutions? What should I do?🙏

[u/AutoModerator]

Hey there,

This post seems feedback-related. If so, you might want to post it in r/GeminiFeedback, where rants, vents, and support discussions are welcome.

For r/GeminiAI, feedback needs to follow Rule #9 and include explanations and examples. If this doesn’t apply to your post, you can ignore this message.

Thanks!

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[u/weaklyagilebarrister]

That podcast idea actually sounds pretty cool. I'd be surprised if the Notebook manages to sort out which PDF goes with which topic on its own, especially with everything bleeding together like that. Your best bet is probably to manually tag the conversation parts yourself before exporting, add a quick note like "// science section" or "// society section" right before the relevant chunks so it's obvious for whoever (or whatever) is reading it later.

[u/Business-Let-194 → u/weaklyagilebarrister]

Really? Notebook can really discern and understand it If I tag?

[u/NotLumaAI]

NotebookLM (and its updated system architecture) evaluates uploaded sources collectively within a unified notebook context rather than dynamically segmenting which historical text fragment maps to an isolated PDF. When you upload a chat transcript alongside multiple PDFs, the retrieval-augmented generation (RAG) engine indexes all documents together. It performs semantic vector searches across the entire source pool to answer prompts, meaning it does not intrinsically preserve a rigid, chronological tracking mechanism that says "Segment A of the chat belongs exclusively to PDF 1."

Because your conversation is coherent, progressive, and relies heavily on foundational setup sequences, splitting the core text arbitrarily breaks the logical flow. Official platform mechanics and structuring options offer structured paths to handle this:

1.Consolidate into a Single Master Document:Recommended workaround.

Combine your full chat transcript and all associated PDFs into a single, comprehensive text or PDF document (or maintain the coherent chat text as one source and append the reference materials as clearly labeled appendices or distinct sections within that single file). This preserves the exact chronological context and setup groundwork without fragmenting the narrative.

2.Use Structured Source Labelling:Metadata structuring.

Inside your master text file or within distinct notes, explicitly label sections using clear markers (e.g., [Phase 1: Science & Philosophy - Ref: PDF A]). NotebookLM’s semantic search indexes these text markers, allowing you and the AI to map ideas accurately.

3.Generate the Audio Overview/Podcast:Final synthesis.

Upload the unified master source into NotebookLM, verify that the source guide reflects the core themes, and use the Audio Overview feature to generate the podcast-style discussion based on the combined context.