How to Analyze Hundreds of YouTube Videos with AI (NotebookLM, Gemini & Claude Workflow)

Modern long-context AI models (such as Google Gemini, NotebookLM, and Claude) can reason across millions of tokens of text—but pasting 100 individual YouTube URLs into a chat prompt is slow, frequently fails on unindexed videos, and gives you no local archive of the underlying text. By extracting the channel locally first with Shirube YouTube Transcript Tool, you can feed clean, metadata-tagged text files or CSVs directly into your AI workflow.

1. Workflow: Bulk Transcripts → Google NotebookLM / Claude / Gemini

Extract & Optional Translate: Run Shirube YouTube Transcript Tool on the target channel. If the channel is in another language, set 🌐 Translate To: English (en) so all transcripts are normalized into English.
For Single-File Long-Context Analysis (Gemini / Claude): Upload transcripts.csv (or filter the top 50 videos by duration/date in Excel). Because every row contains video_id, title, url, and transcript_text, instruct the model: “Cite every claim with the exact video_id and title from the CSV.”
For Timestamp-Cited Deep Dives (Google NotebookLM): Upload the individual transcripts/{video_id}.txt files as sources into Google NotebookLM (official link — no affiliate relationship). Because each .txt file starts with the video title, URL, publication date, and [MM:SS] timestamps, NotebookLM’s inline citations point you to the exact video and minute mark.

Transparency & Affiliate Disclosure: All third-party AI and research tools mentioned in this guide (Google NotebookLM, Google AI Studio / Gemini, Anthropic Claude) are linked directly via their official websites with no affiliate links or commissions.