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Make key pages easy for AI assistants and search engines to read

Rewrite key pages with answer first sections, check structured data in Google's Rich Results Test and decide whether an llms.txt file is worth adding.

Who it suits

Freelance SEO, Small business owner

Time saved

About 1 hour per page reviewed and drafted (estimate)

Level

Medium

Updated

Oct 13, 2026

Why this task

Many service pages bury the answer under long introductions, so readers and machines both struggle to find it. Structured data is often missing, outdated or does not match the page. The agent reviews each key page, drafts clear answer first sections and draft markup, and a person checks and publishes. It also keeps claims about AI search inside what is documented.

You are an editor improving key pages for [BUSINESS NAME], a [BUSINESS TYPE] in [LOCATION]. Readers are [AUDIENCE]. I will paste one page at a time: URL, full text, headings and any existing structured data.

For each page:
1. List the main question each section answers, in the customer's words.
2. Rewrite each section so the first one or two sentences give the direct answer. Keep every fact from the original. Do not add facts, prices, awards, reviews or claims that are not in my text. Mark anything you think is missing as [NEEDS INPUT: what is missing].
3. Suggest a heading for each section that names the question.
4. Flag facts that only appear in images, tabs or downloads, and suggest moving them into plain text.
5. Draft JavaScript Object Notation for Linked Data (JSON-LD) structured data using [SCHEMA TYPES, e.g. LocalBusiness, Service, FAQPage]. Include only properties that match text visible on the page. List any required property you could not fill.
6. Write a short change log: section, old first sentence, new first sentence.

If I ask for an llms.txt file, draft it from the page list I give you: [PAGE LIST WITH ONE LINE DESCRIPTIONS]. Start your reply with this note: llms.txt is a community proposal from llmstxt.org. No assistant has confirmed it as a ranking or citation factor.

Rules: plain words, sentences under 25 words, no hype. Do not promise rankings, rich results or AI citations. Do not claim any search engine or assistant behavior unless I gave you a source for it. Tone guide: [TONE NOTES].

Replace each highlighted placeholder with your own details before you run it.

Steps

1. Pick 5 to 10 key pages: main services, pricing, locations and the most asked questions. Export their text, headings and current structured data with a site crawler.
2. Have the agent list the main question each section answers. Rewrite so the direct answer comes in the first one or two sentences under a heading that names the question.
3. Keep facts that matter in plain text on the page: prices, service areas, opening hours, steps, names. Avoid locking them inside images or tabs that need a click to load.
4. Have the agent draft structured data that matches only what the page visibly says. Google's AI features guidance (https://developers.google.com/search/docs/appearance/ai-features) says structured data should match the visible text.
5. Test the draft markup in the Rich Results Test. Google's structured data introduction (https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data) recommends it for validation and says missing required properties can make markup ineligible for rich results.
6. Publish, then use the URL Inspection tool in Google Search Console to confirm Google sees the page and its markup. Check the rich result reports in the following weeks.
7. Decide on llms.txt. It is a community proposal published at llmstxt.org. No assistant has confirmed it as a ranking or citation factor. Add one only if it is cheap to keep accurate.
8. Record what changed on each page and the date, so later traffic or citation changes can be compared with the edit.

What you get

For each page, a rewritten draft with answer first sections and a list of missing facts for the owner to fill. Draft structured data that passes the Rich Results Test. A dated change log per page, and an optional llms.txt draft clearly labeled as a community proposal.

What can go wrong, and the check for each

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