Prompting AI to Write High-Converting Copy with Competitor Scrapes and Reference Galleries
How do I use AI to rewrite landing page copy based on competitor analysis?
The workflow is a two-step loop: gather competitor copy, then use the AI's synthesis as guardrails for rewriting your own sections.
- Scrape or copy-paste competitor copy and feed it to the AI to clean up.
- Ask the AI what trends it sees across the cleaned data.
- Draw principles from those trends — for example, deciding to put more emphasis on the terms fractional, more emphasis on developers, and less emphasis on e-com managers.
- Go section by section and instruct the AI to rewrite with those principles in mind.
You don't always need a fancy scraper. As one operator put it, sometimes you could just copy paste. Like, you just literally just, like, drag your mouse down. You just copy the thing, and you feed it to the AI. You say, clean this data up. And now you have clean data. That cleaned context is what makes the rewrite useful.
What is the h1 gallery method for AI-generated headlines?
The h1 gallery method trains the AI on a curated corpus of proven headlines before asking it to write yours. The operator describes going to h one gallery, a collection of the best marketing headlines on the Internet, taking all of those h1s, and feeding them to GPT so the model knows what makes a great h1 before drafting headlines for the brand.
The same logic extends beyond headlines: this is h ones, but you can apply similar thinking to h twos and and other types of copy. The point is leveraging external resources that are experts on a given topic to construct a better prompt — see also AI creative engines for auto-generating winning ads.
“Two years ago, like, prompting was a competitive advantage. Today, it's become kind of table.”
How do you extract messaging principles from scraped competitor copy?
Once the competitor copy is cleaned, the AI's job is pattern recognition. You ask it what trends it's seeing across the corpus, and translate those trends into principles — or, as the operator frames them, guardrails almost.
Those guardrails are concrete language calls: which terms to emphasize more, which to emphasize less, which audience to lean into. The principles then ride along in every subsequent prompt: now that you have these principles in mind, rewrite this entire section.
This works because the underlying advantage has shifted. As one advisor notes, two years ago, like, prompting was a competitive advantage. Today, it's become kind of table. The edge now is clean data and a strong semantic layer feeding the model.
“Sometimes you could just copy paste. Like, you just literally just, like, drag your mouse down. You just copy the thing, and you feed it to the AI. You say, clean this data up.”
How does this prompting approach extend to SEO and LLM-visible pages?
The same back-and-forth with AI shapes page structure, not just copy. The operator pastes keyword lists into Semrush, screenshots the results back to the GPT, and lets it recommend which keywords to prioritize.
For new pages, that conversation surfaces Q&A blocks the operator wouldn't have added before — sections like why should you hire a, Shopify developer? What does a Shopify developer do? How much does it cost — placed close to the hero and distinct from the FAQ at the bottom.
The reported result: we get a lot of leads coming to the website. Submitting briefs that have come via the the the LLMs. And that's not because we've done anything fancy specifically to appeal to the LLMs. It's just because we're actually quite good on SEO on... normal organic SEO factors.
Frequently asked questions.
- How do I prompt AI to rewrite landing page copy using competitor analysis?
- Feed the AI all of your competitors' copy — scraped or pasted — and ask it to clean the data and tell you the trends it sees. Turn those trends into principles or guardrails (e.g., emphasize certain terms more, others less). Then walk the AI through your page section by section, instructing it to rewrite each one with those principles in mind. This grounds every draft in observed market language instead of generic LLM defaults.
- What is the h1 gallery method?
- It's a prompting tactic where you train the AI on a curated reference set before asking it to write. The operator pulled headlines from h1 gallery — described as a collection of the best marketing headlines on the Internet — fed them all to GPT, and then asked GPT to write new h1s for the brand. The same approach can be applied to h2s and other copy types: source an expert corpus, prime the model on it, then prompt for your own variants.
- Do I need a scraping tool to get competitor copy into the AI?
- No. There are plenty of AI-powered scraping solutions, but the operator points out that you can often just drag your mouse down a page, copy, paste it into the AI, and ask it to clean the data. The result is usable context for prompting. The mechanism that matters is having clean reference material in the prompt — not the sophistication of how you collected it.
- Is prompt engineering still a competitive advantage?
- Not by itself. As one AI advisor puts it, two years ago prompting was a competitive advantage, but today it's become table stakes. The advantage now belongs to teams with clean data, a strong semantic layer on top of it, and the ability to delegate answers. Quality of taste, evaluation, and your repository of reference material now dictate the quality and quantity of work an AI can generate for you.
- How do I extract messaging principles from scraped competitor copy?
- After cleaning the corpus, ask the AI what trends it sees across competitors' copy. Translate those trends into explicit principles — which terms or audiences to emphasize more, which to dial back. The operator's example: more emphasis on 'fractional' and on developers, less on e-com managers. Keep those principles in the prompt as guardrails, and instruct the model to rewrite each section with them in mind.
- Does this same AI workflow help with SEO and LLM visibility?
- Yes. The operator pastes keyword lists into Semrush, screenshots results back to the GPT, and lets it recommend which keywords to focus on. New pages get Q&A blocks near the hero — answering questions like why to hire a Shopify developer, what one does, and how much it costs — separate from the bottom FAQ. They report leads arriving via LLMs without doing anything fancy specifically for LLMs, just solid organic SEO fundamentals.