Writing prompts for AI Marketer (Aim): best practices and examples

Last updated: September 11, 2026

Building blocks of a good prompt

Tell AI Marketer what to look at, what to measure, how deep to go, and how to structure the answer. A good prompt covers:

  1. Input — The thing to act on: a brand, a URL, a competitor, a list of topics. Use [brand], [competitor], and [topic] as placeholders so a prompt works for any team.

  2. Data source — Where to look and how deep: "top 20 cited pages from the last 30 days", "owned URLs cited on ChatGPT this month".

  3. Task — The specific analysis or decision you want made, not just a summary: "classify each source as press, affiliate, or UGC".

  4. Purpose — The business goal behind the task. It helps AI Marketer prioritize useful findings over noise: "to find partnership opportunities we're missing".

  5. Output format — The deliverable structure: a table with named columns, a ranked list, flagged items. This helps make answers readable and useful.

For example:

(Input) Create an exec-ready gap analysis for [brand] vs [competitor] (Source) across all tracked topics from the past 30 days. (Task) Compare visibility share and citation share by topic, then inspect the top citation pages behind the biggest gaps to identify why [competitor] is winning, (Purpose) so we know where to focus content efforts. (Output) Output a table with: topic, owned and competitor visibility share, owned and competitor citation share. Add a second table with: top citation page, likely driver, confidence, recommended action. Under the tables, add concise bullets on the biggest gaps and priority next steps.

Prompt best practices

  1. One prompt = one job. Keep the prompt focused to a single task. Prompts with multiple tasks like "Review the top 20 cited pages, and then also check whether any affiliate pages have outdated pricing claims" won't return a good result for either.

  2. Give detailed instructions. Explain a task to AI Marketer the way you'd explain it to a less experienced colleague. Vague instructions like "Analyze my data" gives AI Marketer nothing to work with.

  3. Start with a verb: go through, scan, review, identify, compare, flag, output.

  4. Be specific about scope: top 20 pages, last 30 days, top three competitors.

  5. Define the output format: a table, a ranked list, flagged items with columns. Otherwise results become unreadable and inconsistent from run to run.

  6. Include a confidence or rationale column. It makes the output actionable.

  7. Chain the logic: first do X, then for each result do Y, then output Z.

Scale a prompt with Sheets

When you want the same analysis across many topics, URLs, or competitors, run it in a Sheet. Put one input value per row in column A, point the prompt at that column, and AI Marketer runs the prompt for each row in parallel. This works well for topic reports (a gap analysis across 80 topics), content audits (an accuracy check across 200 URLs), and user-generated content sweeps (a Reddit scan across 100 topics).

Learn more in 📄 Getting started with Sheets​.​

Example prompts

Below is the catalog of reusable prompt examples. Copy the prompt text, fill in the placeholders and adjust the scope to your needs.

See where competitors are beating you in AI answers.

Compare [brand] and [competitor] across all tracked topics from the past 30 days on visibility share and citation share. For the topics with the biggest gaps, inspect the top citation pages and explain why [competitor] is winning, so we can prioritize content work. Output a table with: topic, owned visibility share, competitor visibility share, owned citation share, competitor citation share, likely driver, confidence, recommended action. Finish with bullets on the biggest gaps and priority next steps.

Discover which content types earn your citations.

Review the top cited owned pages for [brand] from the past 30 days. Classify each page as blog, product page, FAQ, landing page, comparison, docs, guide, case study, glossary, or other, so we can see which content formats are working. Output an exec summary, key findings, and a table with: URL, content type, citation count, evidence, confidence, recommended action.

Find pages where AI cites you but doesn't name you.

Go through the owned URLs for [brand] that appeared in AI answers this month. Check whether [brand] appears prominently in the title, H1, intro, first paragraph, or key summary sections, so we can fix pages that get cited without clearly saying our name. Output an exec summary, key findings, and a table with: URL, prominence status, evidence, confidence, recommended fix.

Diagnose which owned pages are losing citation share, and to whom.

Scan the top 20 owned cited pages for [brand] from the past 60 days across ChatGPT, Google AI Overviews, Gemini, and Google AI Mode. Identify pages with a month-over-month drop in citation share. For each drop, check which engines and prompts are affected and which replacement sources gained visibility, so we can diagnose lost AI visibility. Output an exec summary, key findings, and a table with: URL, citation share, MoM delta, replacement source, likely cause, confidence, recommended action.

Catch AI misrepresentations of your owned pages.

Take the top 20 cited unique owned pages for [brand] from the past 30 days and scrape each page. Compare the AI responses that cite them against the page content and flag meaningful omissions or contradictions, so we catch brand accuracy issues before they spread. Output an exec summary, key findings, and a table (max 20 priority items) with: URL, engine, prompt, AI claim, page claim, mismatch type, severity, recommended action.

Classify every earned source driving your AI visibility.

Review the top 20 cited pages for [brand] from the past 30 days. Classify each source as press, analyst, review site, affiliate, social/UGC, owned, or other, so we understand which earned channels drive visibility. Output an exec summary, key findings, and a table with: URL, source category, brand sentiment, evidence, confidence, recommended action.

Catch outdated affiliate content that can misrepresent your brand.

Using the [brand] Knowledge Base as the source of truth, go through affiliate and partner pages cited in the past 60 days. Flag outdated or inaccurate claims about positioning, features, pricing, audience, screenshots, or competitors, so partner content doesn't misrepresent the brand in AI answers. Output an exec summary, key findings, and a table with: URL, partner, issue, evidence, severity, confidence, recommended fix.

Monitor reputational risk in AI-cited community content.

Scan social-origin citations for [brand] from the past 30 days and scrape the body text of each page. Flag UGC that mentions [brand] negatively, inaccurately, incompletely, or with reputational risk, so we can monitor brand risk in AI-cited community content. Output an exec summary, key findings, and a table with: URL, UGC type, mention type, risk, evidence, severity, confidence, recommended action.

Find Reddit threads worth engaging.

Review the top cited Reddit threads for [brand] on [topic] from the past 60 days. For each thread, check recency, [brand] mentions, competitor mentions, sentiment, and whether [brand] has a credible opportunity to enter the conversation, so we know which threads to monitor, engage, or support with owned content. Output an exec summary, key findings, and a table with: prompt, subreddit, URL, brand mentioned, competitors mentioned, sentiment, user need, opportunity level, evidence, recommended action.