How to use AI to plan content, and where it falls over
Language models are good at operating on material you already have and bad at inventing material you do not. That single distinction explains most of what works and everything that goes wrong.
8 minute read
The distinction that predicts everything
A language model is good at operating on material you supply and bad at producing material you have not. Clustering eighty raw ideas into themes, summarising three hundred comments, drafting fifteen variants of a title you wrote, outlining from your own transcript — all of these have an input, and the model's job is to rearrange it. Asking what should I post this month has no input, so the model reaches for the average of everything it has read about posting, and hands you exactly that. Almost every disappointing experience with AI planning comes from asking the second kind of question. Once you notice the pattern you can predict in advance whether a prompt is going to be useful, which saves a lot of time.
Clustering is the single best use of it
If you keep an idea backlog, it is probably eighty unsorted lines in a note, which is the state in which ideas are least useful. Paste the whole list in and ask for groupings, with a name for each group and the original lines kept verbatim underneath. This works well because the material is yours, the task is mechanical, and you can check the output at a glance. Two caveats. It will produce tidy-sounding groups that are actually the same group twice, so merge before you commit. And it will sometimes invent a group with one item in it to make the structure look balanced. Ask for fewer groups than feel right, then arbitrate — the sorting is the model's job and the judgement stays yours.
Summarising comment threads and transcripts
This is the second job worth automating, and it saves real hours. Paste two or three hundred comments and ask for the recurring questions ranked by how often they appear, each with two or three verbatim quotes attached. The quotes are not decoration, they are the audit trail: if a supposed theme has no quotes under it, the model made it up, and you will spot that in seconds. The same applies to a long transcript. Ask for the five points where you said something you have not said elsewhere, with timestamps, rather than for a summary. A summary of your own recording tells you nothing you did not already know, and the specific extraction is the thing you actually wanted.
Variations, not originals
Models are usefully good at generating twenty of something when you only need one. Write the title or the hook yourself, then ask for twenty variants along an axis you name — shorter, more concrete, framed as a question, leading with the number, stripped of adjectives. You are using it as a thesaurus at the level of sentences, and picking the one that is better than yours is a fast, low-risk decision. What does not work is asking for a hook from nothing. The default output is the generic register of a thousand marketing blogs: the you-will-not-believe construction, the nobody-is-talking-about-this construction. Those are recognisable as filler within a second, which on short-form is the entire budget you have.
Where it fails: numbers and citations
The most expensive failure is fabricated specifics, and there is hard evidence for how bad it is. Columbia's Tow Center for Digital Journalism tested eight AI search tools by giving them 1,600 queries built from excerpts of real news articles and asking each to name the headline, publisher, date and URL. The tools answered incorrectly more than 60% of the time. Performance varied enormously — Perplexity was wrong 37% of the time, Grok-3 94% — and both Gemini and Grok-3 produced fabricated or broken URLs in more than half of their responses, with Grok-3 returning 154 dead links out of 200 citations. The detail that should change your behaviour: ChatGPT got 134 of 200 wrong while signalling uncertainty just 15 times. Confidence carried no information about correctness.
Where it fails: confident nonsense about platform mechanics
Ask a model what the Instagram algorithm rewards, or the current caption character limit, or whether some feature needs a subscriber threshold, and you will get a fluent, specific, plausible answer that is frequently out of date or simply wrong. The reason is structural rather than mysterious: the training material on these topics is overwhelmingly the same recycled marketing blogs that were already wrong, and platform mechanics change faster than any training cycle. Worth noting that YouTube's own Inspiration tab, which generates ideas, titles, hooks and outlines inside Studio, carries a caution from YouTube that its suggestions may be inaccurate and vary in quality. When the platform will not vouch for its own generated advice about itself, treat every specification claim as a prompt to go and read the help page.
The editorial pass is not optional
Say it plainly: content written by a model and published without editing reads like it, and performs like it. Practitioners already know this. HubSpot's 2025 State of AI for Marketers survey found only 7% publish AI-generated content without revision, while 56% significantly rewrite it and 38% make minor adjustments — and 43% named inaccurate information as a problem they run into. The platforms have converged on the same position from the other direction. Google's guidance says AI use is fine and content is judged on quality, but generating many pages without adding value falls under its scaled content abuse policy. YouTube renamed its repetitious content rule to inauthentic content in July 2025 and requires monetised work not be mass-produced, generic, repetitive or manipulative. Neither penalises the tool. Both penalise output nobody contributed to.
A workflow that keeps the judgement with you
Five rules cover most of it. Supply the raw material rather than asking for it. Ask for structure, not substance. Demand verbatim quotes for anything the model claims is in your source. Ask for ten options and choose, rather than one option and accept. And never let a model be the origin of a statistic — if you cannot find it on the publisher's page yourself, it does not go in. The broader point is where you aim the automation. The mechanical steps of video work — transcribing, surfacing candidate moments, reframing to vertical, generating captions to correct — are much safer ground for a machine than deciding what is worth saying. FrameOS is built on that split, and it is an honest description of the limit rather than a modest one: it removes chores, it does not have taste.
FAQ
Will Google or YouTube penalise AI-generated content?
Not for using AI. Google's published guidance says content is judged on quality regardless of how it is produced, but warns that generating many pages without adding value may violate its scaled content abuse policy. YouTube renamed its repetitious content rule to inauthentic content in July 2025 and requires monetised content not be mass-produced, generic, repetitive or manipulative. The tool is not the issue; unedited volume is.
Can AI come up with good video ideas?
It is good at organising ideas you already have and weak at inventing new ones. Clustering a messy backlog into themes, or pulling recurring questions out of three hundred comments, produces genuinely useful output because the material is yours. Asking an empty what should I post prompt returns the average of everything it has read, which is why those answers feel generic.
Can I trust AI for statistics in my content?
No. Columbia's Tow Center tested eight AI search tools across 1,600 queries and found incorrect answers more than 60% of the time, with fabricated or broken URLs in over half of Gemini and Grok-3 responses. ChatGPT was wrong on 134 of 200 items while flagging uncertainty only 15 times. Verify every number on the publisher's own page before you use it.
What is the best way to prompt AI for content planning?
Give it your material and ask it to rearrange rather than invent. Paste the backlog, the comments or the transcript, ask for groupings or extractions with verbatim quotes attached, and request ten options so you can choose. Quotes give you an audit trail, and choosing keeps the editorial decision where it belongs.
Sources
- AI Search Has a Citation Problem · Columbia Journalism Review
- AI in content marketing: How creators and marketers are using AI · HubSpot
- Google Search's Guidance on Generative AI Content on Your Website · Google Search Central
- YouTube channel monetization policies - YouTube Help · YouTube Help
- Explore Inspiration tab on YouTube - YouTube Help · YouTube Help
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