Using ChatGPT for Writing YouTube Scripts Without Losing Context

Table of Contents

1. Building a prompt template that survives multiple script styles

The first time I asked ChatGPT to write a YouTube video script, I got three paragraphs of generic advice about lighting and speaking clearly. It thought I wanted tips for being a YouTuber — not a script. Once I clarified, it still didn’t get the blend of tone I needed. That’s when I started stacking role directives into the prompt — almost like nesting hats — which honestly confused GPT3.5 but works better on GPT-4 (most days).

It turns out, you absolutely need to lock down a few things up front:

A role: e.g., “You are a YouTube scriptwriter who specializes in educational content with deadpan humor.”
A goal: “Your job is to write a full YouTube video script from a single title or idea prompt.”
Instructions for formatting: “Use plain text, include camera cues occasionally, and time for voice pacing.”
Optional: tone annotations like “match the cadence of Tom Scott but with 20% more sarcasm.”

A nowhere-documented behavior I ran into is that GPT will drift stylistically by the third message in a thread. That’s not a model flaw, it’s a byproduct of how it semi-remembers the conversational context without applying parameter locks. If your tone and structure matters, start a new thread, paste the full template every time. Repeat the prompt from scratch. It feels inefficient but stops the Android-level tone creep.

2. What happens when you paste URLs into the script prompt

I had a folder of five Google Docs titled roughly like “vid_ideas_final_maybe.” I dumped their URLs into the same prompt window and asked ChatGPT to combine them into one tight five-minute script. This… sort of worked — except GPT started hallucinating content that wasn’t in any of the docs.

The issue is simple: pasting a URL tells ChatGPT absolutely nothing unless it’s in a context where the model has embedded access (e.g., via a plugin, browser tool, or GPT with browsing enabled). If there’s no live access, it just ignores links and guesses what they contain based on filename and pattern matching. That’s how it hallucinated an entire segment on “why AI-generated pizza is a metaphor for society.”

The fix: paste the actual content, not the links. If that’s too messy, use a browser plugin like AI PRM or a Notion to ChatGPT bridge that embeds the text. Or pre-clean your notes into something that fits under the model’s context token limit — around 8k for GPT-4-turbo right now, which is close to 5000–6000 words depending on structure.

3. Using Markdown for staging script sections in ChatGPT threads

One of the best micro-tricks I’ve found is to scaffold the response structure using Markdown before asking for generation. I’ll literally say:

Write a script using the following section headers:

## Intro

## Segment One: The Old Workflow

## Segment Two: What Broke

## Segment Three: How AI Helped

## Outro

Write it in a conversational tone, first-person.

That pre-structure gives the model just enough rigidity to hold the sections together. Without it, you risk blending segments or getting mid-sentence topic jumps. Ask ChatGPT to rewrite just one section if it drifts too far off-tone — don’t start over. For example, when Segment Two went full project-manager vibe (“leveraged KPIs to achieve asynchronous success”), I just replied: “Redo Segment Two. Make it sound like it was written in a coffee shop with bad WiFi and four deadlines.” The next take was usable.

4. Token limits cut narration mid-punchline without warning

About halfway through a long video idea involving smart fridges, ChatGPT just… stopped. The final line was literally “and that’s why every banana—”. That’s when I realized I hadn’t chunked the request properly. Token limits are real, but worse, the UI won’t warn you until the model hits the ceiling and freezes out. The cut-off is often mid-sentence.

To avoid this, design your prompt around a series of segments rather than a full monologue. Something like:

“Write the first 90 seconds of the intro to this title.”
“Now write the next section, continuing the same tone.”
“Summarize Segment Two in a single dry punchline.”

This staggered generation has another upside: it lets you course-correct as the model drifts. If GPT starts leaning TED Talk at any point, you can yank it back mid-run instead of regenerating everything.

5. Why custom GPTs sometimes forget their own instructions mid-script

I made a custom GPT in the playground once. Spent a solid 40 minutes dialing the behavior instructions to a T: always include a cold open, never use motivational language, include cutaway jokes in brackets. First round of output? Nearly perfect. Second? It started ending paragraphs with “Remember, you’ve got this!” What??

Turns out: when using the custom GPT feature, there seems to be a weighting issue where behavioral instructions decay more rapidly over longer prompts — especially when inputs go over a couple thousand tokens. The model begins to “prioritize” the end-user message more than the built-in role behavior if the form input is structured poorly.

The workaround I’ve landed on:

Keep built-in instructions short + firm (“No motivational advice. Never break format.”)
In your prompt, echo a short version of the behavior again

Use structured segment layouts like

Section One
[Cold open with dark sarcasm]

Section Two
[Visual cue: overly complicated diagram]

It also helped to regenerate from zero once per session. There’s invisible session persistence that sometimes pulls past biases into future prompts. Same GPT, fresh thread, totally different energy.

6. Single-space spacing resets when copying scripts to YouTube Studio

This one genuinely wasted 80 minutes of my life. I wrote a full script using single spacing between lines in ChatGPT, did final edits in Notion. Pasted it into YouTube Studio’s script upload field — and every single line got crammed together with zero spacing. Looked like a block of text with no breaks. It wasn’t rendering Markdown or respecting newline characters.

The fix is weirder than I expected. Rather than trying to add more spacing inside ChatGPT, I exported the final script to Google Docs, then used the copy-as-plain-text option. That preserved the manual breaks without importing hidden styles. You can also paste into a text editor like VS Code and add \n manually if you want fine control before pasting.

Basically: the spacing resets unless your source layer renders raw newlines instead of invisible rich text spacing. Studio doesn’t care how clean your layout was if the paste origin had view-only markdown styles. And the worst part — there’s no preview until you scroll back and realize the whole intro looks like a press release.

7. The subtle setting that resets creativity temperature without notice

Default ChatGPT sessions used to start with a temperature around 0.7. When using GPTs or slightly older versions, I got used to outputs being mildly creative but still predictable. Lately, I’ve noticed it swinging between clinical and unhinged — and I finally tracked it down: if you use an embedded ChatGPT inside a browser extension (I was using one to auto-sync Notion notes), it silently sets temperature to 0.3. That means lower randomness, flatter tone, more repetition, and worse punchlines.

No visible setting warns you. There’s no banner, no mention in the UI. But you can test it easily: paste the same exact prompt into a web-based ChatGPT window and your extension — watch the tone shift. Extensions and integrations often set temperature or max tokens by default based on internal APIs, and those can override OpenAI defaults without warning the user.

The workaround? Assume the tool you’re using might silently rewrite prompt parameters unless it explicitly shows config options. Better tools (like plain Zapier integrations or full API workflows) let you control temperature manually. The browser plugin I bailed on last month didn’t even mention it in the README.

Here’s the actual behavior logged from one session token:

“completion_config”: {
“temperature”: 0.3,
“max_tokens”: 2048,
“top_p”: 1.0,
“presence_penalty”: 0.0,
“frequency_penalty”: 0.0
},

You know how that output started? “Hey friends, let’s talk about…” — and I never said “friends.”

8. Mistiming voiceover pacing ruins edit timing later

If you script with ChatGPT, expect to adjust your expectations on timing. I once had a “90-second” intro that was almost 700 words long in the draft. After voiceover? It blew past three minutes. The script sounded tight because the lines read fast — but human speech adds pauses, emphasis, breaths, and camera cuts.

Now every time I write a script using ChatGPT, I chunk the output and estimate the time live as I read it out loud. Not even fancy — just stopwatch on the phone, read it like I’m recording for real. If a paragraph takes more than 20 seconds, I flag it as too fat. I even started dropping voice pacing markers into the prompt:

Write a 3-minute video script.
Assume reader pauses slightly every 10 seconds.
Include beat breaks or emotional pivots every paragraph.

That cue alone improved timing accuracy by a lot — maybe not to the second, but close enough that I wasn’t shifting entire segments mid-edit. For batch scripting, it keeps VOs within editing range. Plus, it kills filler — GPT tends to waffle less when it knows it has to hit pacing beats.