If you make YouTube videos regularly, the hard part is not always recording. A lot of time disappears before you ever hit record: shaping the idea, breaking it into scenes, writing the script, estimating the runtime, making metadata, and keeping track of where the project lives.
I have been using GPTs for scripting, coding, and content planning for a while, and I recently rebuilt my Life With Tech workflow into one custom GPT called the Life With Tech Content Engine. The goal was simple: stop bouncing between too many tools and make one place that helps move a video from rough idea to usable production materials.
Quick Answer
My custom GPT saves time by turning a video topic or rough title into a structured YouTube workflow: show selection, video type, scene breakdown, teleprompter script, runtime estimate, metadata, thumbnail placeholder, and an exportable Markdown file.
It does not replace editing, judgment, or final creative decisions. I still tweak the script, adjust the title, review the description, and usually replace the thumbnail. But it gives me a strong working draft fast enough that I can focus more on making the video instead of organizing the video.
What The GPT Is Built To Do
Inside ChatGPT, I created a custom GPT called the Life With Tech Content Engine. It has three main starting points: create a YouTube video, format for teleprompter only, and write a blog post.
For this walkthrough, I used the YouTube video path because that is where most of the workflow comes together. The GPT first asks what show the video is for. In my case, it defaults to Tech Bits, which is one of the formats I use.
Then it asks whether I have a transcript, topic idea, or summary to work from. Sometimes I already have a script and bring it back into the GPT to clean up or structure. Other times, I only have a topic or title idea.
Starting From A Rough Topic
In the demo, I started with a sample title: ChatGPT versus Perplexity: Which AI replaces Google Search better? That was enough for the GPT to start building a video structure.
The next question is whether the video is pre-recorded or live. I set this up intentionally because those two formats need different outputs. A pre-recorded video gets a more complete teleprompter-style script. A live video would be better as bullet points or talking prompts.
After that, the GPT asks whether I already have a scene list or rough structure. If I do, I can provide it. If I do not, it defaults to a five-scene structure.
The Scene Breakdown
The default five-scene structure is one of the most useful parts of the workflow. Instead of staring at a blank page, I get a simple shape for the video right away.
For the ChatGPT versus Perplexity example, it created a hook around Google Search not feeling as useful anymore, a short personal setup about trying Perplexity, a head-to-head comparison section, an engagement question, and a call to action.
That structure is not locked in. I can keep it, rewrite it, remove scenes, or give the GPT more direction before it starts writing the actual script.
- Scene 1: Hook and pain point
- Scene 2: Brief personal story or setup
- Scene 3: Main comparison or explanation
- Scene 4: Viewer question or engagement point
- Scene 5: Call to action or next step
Writing In A Teleprompter Format
Once the structure is approved, the GPT starts writing scene by scene in a canvas. That matters because the script is not just buried in the chat thread. It becomes an editable document on the side where I can make changes directly.
For each scene, it gives me a teleprompter-style section and a rough runtime estimate. Those estimates are not perfect because everyone speaks at a different pace, but they help me see whether the video is turning into a quick three-minute piece or something much longer.
If a scene feels too long, I can ask for a shorter version. In the demo, I asked it to make the opening under 15 seconds, and it rewrote the hook into a tighter version. I can also edit the canvas manually and delete or rewrite lines myself.
Why Runtime Estimates Help
Runtime estimates are one of those small workflow details that make a big difference. When I am planning a video, I want to know early whether the structure matches the kind of video I intended to make.
In the example, the GPT estimated each scene and then gave a full video estimate of around three minutes. That lets me make practical decisions before recording. If I wanted a shorter quick tip, I could cut sections. If I wanted a deeper comparison, I could expand the main breakdown.
The key is that the estimate is a guide, not a final measurement. It gives me a feel for the video before I spend time recording it.
Metadata And Titles
After the script is finished, the GPT moves into metadata and thumbnails. It suggests an alternate title, writes a description based on the script, includes my subscription link, and generates tags.
I still review this part carefully. In the demo, it suggested a title about which tool replaces Google better. I did not like the phrasing because I did not want the video to sound like it was about replacing Google entirely. I asked it to use Google Search instead, which made the title more accurate.
That is the kind of adjustment I expect to make. The GPT gives me a starting point, but I still need to make sure the title and description match what I actually mean.
Thumbnail Placeholders
The GPT can also generate a thumbnail idea or placeholder image. I treat that as a temporary asset, not the final thumbnail.
In the walkthrough, the thumbnail was hit and miss. It got the ChatGPT side mostly right, but it did not use the real Perplexity logo. That is a good example of where AI can help with a placeholder, but I would still replace it with something more polished before publishing.
The placeholder is still useful because I can attach it to a reminder or project item while the video is moving through production. It gives the project a visual marker even before the final thumbnail is done.
Exporting The Script
Once the script and metadata are ready, I have the GPT create a Markdown file. That gives me a downloadable version of the script that I can keep with the rest of the project materials.
I also use the ChatGPT share link. I copy that link and save it into Reminders so I can get back to the exact GPT conversation later if I need to revise the script, change metadata, or reuse part of the workflow.
Why I Simplified The Workflow
I used to put more of this into Bear and ClickUp. Those tools can be useful, but for this workflow I wanted fewer moving parts.
Right now, I like keeping it simple. The GPT creates the working materials, and Reminders helps me track where each video is in the process. I use a Kanban-style structure, so a video can move from scripting to recording and through the rest of the production flow.
That makes the system easier to maintain. The important part is not having the fanciest project management setup. It is having a workflow I will actually use.
How The Custom GPT Is Set Up
The custom GPT itself is built from a name, description, main instructions, and conversation starters. The conversation starters are the buttons that launch the different workflows, like creating a YouTube video or formatting something for the teleprompter.
The real work is in the instructions. I have tweaked those over time through trial and error so the GPT understands the kind of output I want and the way I tend to structure Life With Tech videos.
That tuning is why it feels more useful than opening a blank ChatGPT chat and typing the same prompt over and over. The workflow is already built in.
Key Takeaways
- A custom GPT can turn a rough YouTube topic into a structured script workflow.
- Separating pre-recorded videos from live videos helps produce the right kind of output.
- Scene-by-scene writing makes it easier to revise, shorten, or expand a video before recording.
- Runtime estimates are rough, but useful for planning the length of a video.
- AI-generated thumbnails are better as placeholders than final publishing assets.
- Keeping the GPT link and project details in Reminders helps simplify the production workflow.
Watch the Video
The video above for the full walkthrough of the Life With Tech Content Engine, including the scene-by-scene script generation, metadata step, thumbnail placeholder, Markdown export, and how I save the project back into Reminders.
