How to take notes from YouTube videos (using AI)

TL;DR

Use an AI transcriber configured to capture system audio so YouTube playback becomes an automatic transcript.

Briefing Cornell Notes

Briefing

Automatically turning YouTube audio into structured study notes is the core workflow here: play a video, transcribe it from system audio, and then run the transcript through a Reflect custom prompt that outputs a summary, key takeaways, and a spot for the viewer’s own in-the-moment notes. The practical payoff is less re-typing and less time wrestling with raw transcript text—what arrives first is a “giant block of text,” and what arrives next is formatted notes that can be dropped straight into a daily or dedicated note.

The process starts with an AI transcriber configured to capture system audio from the computer running the YouTube playback. The user watches an “intro to paragliding” video while transcription runs in parallel, so the transcript is generated without manual typing. Crucially, the workflow supports interruption: the viewer can pause the video at any point, speak additional notes, and then resume. Those spoken insertions are wrapped in explicit markers—“note from [name]” and “end note”—so the later AI formatting step can preserve them as a distinct section rather than burying them inside the transcript.

Once the transcription is complete, the transcript is sent into Reflect using a custom prompt designed specifically for video and podcast note-taking. The prompt is organized into three sections: (1) a summary of the whole video, (2) main ideas and essential information presented as key takeaways, and (3) the viewer’s own inserted notes. Formatting instructions are part of the prompt as well, including markdown-style headers and layout preferences like indentation and bolding. To make the output consistent, the prompt includes an example of the desired structure, and it can optionally generate a title (in this run, it keeps a default title like “summary of notes”).

After the AI finishes, the output is reviewed and then converted from markdown into the final note format inside Reflect. The result includes the summary, the key takeaways, and the backlinked personal note segment—turning the transcript into something immediately usable for review.

The workflow also addresses how to keep the source context. One option is saving the video link via a Chrome extension, but the user’s typical approach is to keep only the notes and then rename or copy them into an existing note collection. The “magic,” according to the workflow, is the interactive feel of taking notes while watching: pause, dictate, and continue—so the notes behave more like a guided class than a one-time transcript dump.

Overall, the method is positioned as a reusable template: clone the Reflect custom prompt (via Command J and the prompt expansion UI), edit it to match personal preferences, and apply it to future learning from YouTube. The emphasis is on capturing knowledge as it’s consumed, not after the fact, and producing notes that are structured enough to revisit later without rereading the entire transcript.

Cornell Notes

The workflow turns YouTube system audio into structured notes in Reflect. While watching, a transcriber captures the video audio, and the viewer can pause to dictate extra notes wrapped in markers like “note from [name]” and “end note.” After transcription, a Reflect custom prompt formats everything into three sections: a brief summary, key takeaways (main ideas and essential information), and the viewer’s inserted notes. Formatting rules (markdown headers, indentation, bolding) make the output consistent and reviewable. This matters because it replaces a raw transcript “block of text” with notes that can be dropped into daily or dedicated study workflows.

How does the workflow capture a YouTube video without manual transcription?

It uses an AI transcriber that listens to system audio—specifically the audio coming from the YouTube playback on the user’s device. The user starts transcription, plays the YouTube video, and lets the transcriber generate the transcript while watching, avoiding typing during the session.

What mechanism lets the viewer add personal notes during playback?

The viewer pauses the video and speaks additional content wrapped in explicit markers: “note from [name]” and “end note.” Those markers signal to the later formatting prompt that the spoken segment should appear as the viewer’s own notes section, separate from the auto-transcribed material.

What does the Reflect custom prompt produce from the transcript?

The custom prompt is set up to output three parts: (1) a summary of the entire video, (2) key takeaways listing main ideas and essential information, and (3) the viewer’s inserted notes (the segments marked “note from [name]” to “end note”). It also includes formatting instructions such as markdown-style headers and indentation.

Why include formatting examples and markdown instructions in the prompt?

The prompt includes a sample structure and layout rules so the AI returns consistent, nicely formatted notes rather than an unstructured transcript. The user can then convert the markdown output into the final note format inside Reflect and quickly scan the result.

What are the practical ways to store or reuse the generated notes?

The user can optionally save the video link using a Chrome extension, but often they keep only the notes. They may rename the note for a daily log (e.g., “video on PG ground handling”) or copy it into a dedicated learning note, with the main remaining effort being setting up the prompt once.

What makes the workflow feel different from a plain transcript tool?

It supports interactive note-taking while watching—pause, dictate, and continue—so the notes reflect both the video content and the viewer’s immediate questions or actions. That turns learning into an “in-class” style experience rather than a one-shot transcript dump.

Review Questions

What steps are required from starting transcription to getting formatted notes in Reflect?
How do the “note from [name]” and “end note” markers affect the final output structure?
What three sections does the custom prompt generate, and how does formatting guidance change the usefulness of the result?

Key Points

1
Use an AI transcriber configured to capture system audio so YouTube playback becomes an automatic transcript.
2
Pause during playback and dictate personal inserts wrapped in “note from [name]” and “end note” markers.
3
Run the transcript through a Reflect custom prompt that outputs a summary, key takeaways, and a dedicated section for your inserted notes.
4
Include formatting rules (markdown headers, indentation, bolding) and an example structure in the prompt to keep outputs consistent.
5
Convert the AI’s markdown output into the final note format inside Reflect for easier reading and reuse.
6
Store notes by renaming for daily capture or copying into dedicated learning notes; optionally save the video link via a Chrome extension.
7
Treat the prompt as a reusable template: clone it in Reflect, edit it to match preferences, and apply it to future YouTube learning.

Highlights

The workflow captures YouTube notes by transcribing system audio while the video plays, eliminating manual typing.

Personal notes become first-class content by using explicit “note from [name]” to “end note” markers during pauses.

A Reflect custom prompt turns a raw transcript block into three structured sections: summary, key takeaways, and your inserted notes.

Formatting instructions and a sample template in the prompt produce consistent, scan-friendly markdown output.

The biggest advantage is interactive learning: pause, dictate, and continue—so notes feel like a guided session.

Topics

YouTube Transcription
Reflect Custom Prompts
System Audio Notes
Key Takeaways
Markdown Formatting

Mentioned

Reflect