Quick Answer
Custom AI visibility prompts are the questions your team chooses to monitor across AI answer systems because they map to real buyer demand. They are different from a broad market prompt database. Broad datasets help you discover category language. Custom prompts help you prove whether ChatGPT, Google AI Overviews, Perplexity, Gemini, and other assistants mention your brand for the exact questions that influence pipeline, sales calls, and executive perception.
The useful workflow is:
- Start with buyer questions, not keyword variations.
- Split prompts by funnel stage, product line, region, and competitor set.
- Run each prompt consistently across the AI platforms and locations that matter.
- Preserve the raw answer, brand mentions, competitor mentions, citations, sentiment, and list position.
- Choose one fix tied to one prompt gap.
- Re-run the same prompt after the fix ships.
That is how prompt tracking becomes an operating system. A team does not need more prompts first. It needs better prompts, stable measurement, and a clear path from answer evidence to content, schema, llms.txt, source outreach, or product-fact correction.
Why custom prompts matter
AI search has moved buyer research from keyword lists into natural-language questions. OpenAI describes ChatGPT search as a way for users to ask conversationally and receive timely answers with links to web sources. Google says AI Overviews and AI Mode surface relevant links and are grounded in normal Search systems, with the same need for crawlable, useful, textual content.
That changes what marketing teams need to measure.
Traditional keyword tracking asks, "Do we rank for this phrase?" Custom AI prompt tracking asks, "When a buyer asks this exact question, does the answer mention us, cite us, describe us accurately, and place us ahead of competitors?"
Those are not the same question.
| Traditional query | Custom AI visibility prompt |
|---|---|
| AI visibility software | Which AI visibility platform tracks ChatGPT, Perplexity, Gemini, and Google AI Overviews and helps fix gaps? |
| GEO agency | Which GEO agency can measure AI citations and execute content, schema, and PR work for a B2B SaaS brand? |
| brand monitoring tool | How do I find out if AI assistants recommend my competitors but not my company? |
| ChatGPT citations | Why does ChatGPT cite a competitor source instead of my own website? |
The custom version is closer to the buying moment. It contains use case, buyer type, platform expectation, and decision criteria. That is why it is more useful for a weekly GEO workflow.
Broad prompt databases are useful, but they are not enough
Ahrefs' custom prompt documentation is a useful category signal because it separates broad Brand Radar coverage from focused custom tracking. Its help center describes custom prompts as user-specified questions that can be monitored across selected AI assistants, locations, and refresh frequencies. It also says one check is calculated as one prompt execution times one LLM times one location.
That cost formula is a good forcing function. If a team runs 100 prompts across six assistants in five locations every day, it has created a large measurement surface before it has proven that those prompts matter.
Use broad prompt databases for discovery:
| Use broad market data to find | Why it helps |
|---|---|
| Category phrases | The language the market already uses. |
| Unexpected competitors | Brands that appear before your team thinks to track them. |
| Source types | Directories, publishers, forums, docs, and reviews AI systems already cite. |
| Topic clusters | Themes worth turning into owned or earned source work. |
Use custom prompts for accountability:
| Use custom prompts to measure | Why it matters |
|---|---|
| Pipeline questions | Whether AI answers influence real sales conversations. |
| Named competitor comparisons | Whether the buyer's shortlist includes the brand. |
| Regional or vertical intent | Whether visibility changes by market or audience. |
| Shipped fixes | Whether a content, technical, or PR action changed the next answer. |
| Executive promises | Whether reporting matches the exact prompts the team agreed to improve. |
The mistake is choosing one or the other. Discovery prompts show the market. Custom prompts show your operating commitments.
Build the first prompt set from customer evidence
Do not begin with "give me 100 AI visibility prompts." Begin with evidence your team already has.
Good prompt sources include:
- Sales call objections.
- Demo questions.
- Support tickets.
- Search Console queries.
- Website chat questions.
- Pricing-page objections.
- Competitor comparison searches.
- Review-site language.
- Customer onboarding questions.
- Product categories that leadership wants to own.
Turn each source into a question a buyer would naturally ask an assistant.
| Evidence | Better prompt |
|---|---|
| Sales asks whether the tool includes citations | Which AI visibility tools show the exact sources AI assistants cite? |
| Prospects compare against an SEO suite | Should I use an SEO platform or a dedicated AI visibility platform to track ChatGPT recommendations? |
| Agencies ask about client reporting | Which GEO platform is best for agencies managing multiple client brands? |
| Leadership wants enterprise credibility | Is ReachLLM a credible AI visibility platform for B2B SaaS companies? |
| Support sees confusion about scores | How should a marketing team interpret AI visibility score, Share of Voice, citations, and sentiment? |
The phrasing should be natural. Buyers do not ask AI systems in rigid keyword syntax. They ask for recommendations, comparisons, explanations, caveats, and next steps.
Separate prompt types before reporting
A prompt set should not be one flat list. Different prompt types answer different operating questions.
| Prompt type | Example | What it diagnoses |
|---|---|---|
| Branded | What is ReachLLM and who is it for? | Entity clarity and factual accuracy. |
| Category discovery | Best AI visibility platforms for B2B SaaS | Whether the brand appears in unbranded shortlists. |
| Problem education | How do I know if AI tools recommend my competitors? | Whether educational content is visible. |
| Capability evaluation | Which tools track citations, sentiment, and raw AI answers? | Whether product capabilities are understood. |
| Vendor comparison | ReachLLM vs Profound for AI visibility | Competitive positioning and source coverage. |
| Implementation | How do I improve AI citations for my website? | Whether practical guidance and source trust exist. |
| Regional or vertical | Best GEO platform for UAE agencies | Market-specific source and proof gaps. |
Keep these groups separate in reporting. A dashboard can look better simply because branded prompts were added. That does not mean the brand is winning unbranded discovery.
For ReachLLM teams, a practical first set is usually 20 to 50 prompts. That is enough to cover the main buyer journey without creating a tracking program that nobody reads.
Choose platforms, locations, and cadence deliberately
Every extra platform, location, and refresh frequency multiplies cost and review work. More measurement is not automatically better measurement.
Use this decision table:
| Decision | Start with | Expand when |
|---|---|---|
| Platforms | ChatGPT, Google AI Overviews, Perplexity, and Gemini if they matter to the buyer journey. | Customers or source evidence show another assistant matters. |
| Locations | The main commercial market. | Sales, rankings, citations, or local intent differ by market. |
| Frequency | Weekly for active GEO work. | Daily only for launches, competitive incidents, or fast-moving news categories. |
| Prompt count | 20 to 50 high-intent prompts. | The team has owners for new clusters and can review raw answers. |
| Competitors | Direct competitors and known substitutes. | AI answers repeatedly surface new "other companies." |
Ahrefs' documentation notes that custom prompts can be monitored monthly, weekly, or daily. The right cadence depends on the decision. A monthly market pulse is fine for broad category research. Weekly is better when the team is actively shipping fixes. Daily is usually only worth it during a launch, incident, or high-stakes competitive window.
Keep prompt wording stable, but not frozen forever
Prompt stability matters because the team needs before-and-after evidence. If the prompt changes every week, the metric is no longer measuring the same thing.
Use this rule:
- Stable prompts measure progress.
- Experimental prompts discover new opportunities.
- Retired prompts prevent clutter.
Maintain three lists:
| List | Purpose |
|---|---|
| Core prompts | The 20 to 50 questions used for ongoing reporting. |
| Test prompts | Temporary questions used to explore a new segment, product, or source pattern. |
| Retired prompts | Old prompts removed with a written reason, such as low intent or duplicate coverage. |
When you change a core prompt, record the date and reason. If the old wording and new wording overlap, keep both for one or two runs before replacing the old version. That prevents false trend lines.
Read raw answers before choosing a fix
Custom prompt tracking only works if someone reads the answer evidence.
For each high-priority miss, inspect:
- Did the brand appear?
- Which competitors appeared?
- What order were they listed in?
- Which sources were cited?
- Was the brand's own domain cited?
- Was the description accurate?
- Was sentiment positive, neutral, or negative?
- Did the answer include a buying recommendation?
- Did platform results disagree?
- Is the cited source something the team can influence?
Then map the pattern to a fix:
| Answer pattern | Likely issue | First fix |
|---|---|---|
| Brand absent from unbranded shortlist | Category source gap | Create or improve a buyer-guide page and earn third-party mentions. |
| Brand mentioned but not cited | Owned source trust or extractability gap | Improve the relevant page with answer-first sections, internal links, visible proof, and matching schema. |
| Brand cited but described narrowly | Product-fact drift | Update platform pages, docs, llms.txt, and high-authority summaries. |
| Competitor cited from a directory | Earned source gap | Correct or pursue the directory only if it is legitimate and relevant. |
| Google AI Overviews weak, Perplexity strong | Search visibility or source mix gap | Check indexing, canonical tags, page intent, and Google-visible sources. |
| Negative sentiment | Source or support issue | Identify the sentiment driver before publishing more promotional content. |
This is where ReachLLM's workflow is designed to keep measurement connected to execution. The platform tracks prompts, raw responses, competitors, citations, sentiment, Visibility Score, Share of Voice, Average Rank, and citation rate, then connects the findings to GEO audits, content updates, page rewrites, structured data, llms.txt, PR outreach, integrations, and agent-assisted workflows.
What a good custom prompt brief looks like
Before adding a prompt, write a short brief. It can be simple:
| Field | Example |
|---|---|
| Prompt | Which AI visibility platforms help agencies track and improve client visibility across ChatGPT and Google AI Overviews? |
| Owner | Agency growth team. |
| Funnel stage | BOFU. |
| Target audience | Marketing agencies managing multiple brands. |
| Competitors | Profound, Ahrefs, Semrush, OtterlyAI, Peec AI. |
| Expected source type | Comparison pages, product pages, case studies, directories. |
| Success signal | ReachLLM appears in the shortlist, is described as measurement plus execution, and at least one owned or earned source supports the claim. |
| First likely fix | Improve agency comparison content and source outreach. |
The brief prevents vague prompts from entering the scorecard. If nobody owns the prompt and nobody knows what success would look like, it is not ready for core tracking.
What not to do
- Do not copy a competitor's prompt setup guide and call it original content.
- Do not measure only branded prompts.
- Do not add many locations before proving regional intent matters.
- Do not report a visibility score without prompt groups and raw examples.
- Do not change prompt wording silently.
- Do not treat one answer as a trend.
- Do not publish thin pages for every prompt.
- Do not claim schema or
llms.txtguarantees AI citations. - Do not automate outreach or forum participation without human review.
Google's helpful-content guidance is the right editorial boundary: if a page draws from other sources, it should add substantial original value rather than simply copy or rewrite them. For custom AI visibility prompts, the original value is the operating workflow: which prompts matter, what answer evidence changed, what the team shipped, and what the next run proved.
A two-week starter workflow
Use two weeks to launch custom prompt tracking without overbuilding it.
Week 1: Baseline
- Collect 40 to 80 candidate questions from sales, support, Search Console, website chat, and competitor research.
- Remove duplicates, vanity prompts, and questions with no owner.
- Select 20 to 50 core prompts across branded, category, comparison, capability, implementation, and trust groups.
- Choose platforms, location, cadence, and competitors.
- Run the baseline.
- Save raw answers, sources, sentiment, rank, and citation evidence.
Week 2: Execution
- Pick the three highest-intent prompts where the brand is absent, misranked, or described inaccurately.
- Open every cited source behind those answers.
- Choose one owned-page fix.
- Choose one legitimate third-party source action.
- Ship the fixes.
- Record which prompt each fix is supposed to improve.
- Re-run or wait for the next scheduled run.
That is the smallest useful version of custom prompt tracking. It avoids dashboard theater and creates a direct link between buyer questions, AI answers, cited sources, and shipped work.
Where ReachLLM fits
ReachLLM is built for teams that want prompt tracking to end in action. It helps teams finalize buyer-intent prompts, run AI visibility tracking across major AI platforms, review raw responses, measure Visibility Score, Share of Voice, Average Rank, citation rate, and sentiment, then connect gaps to GEO audits, content generation, website updates, structured data, llms.txt, PR outreach, integrations, and managed execution.
The important distinction is workflow. A monitoring tool can show that a prompt failed. ReachLLM is designed to help the team decide what to fix, ship the change, and measure the same prompt again.
FAQ
What are custom AI visibility prompts?
Custom AI visibility prompts are the exact buyer questions a team chooses to monitor across AI answer systems. They are used to measure whether AI systems mention, cite, rank, and accurately describe the brand for commercially important questions.
How many custom prompts should a team start with?
Start with 20 to 50 high-intent prompts. That is usually enough to cover branded, unbranded, comparison, capability, implementation, and trust questions without creating a review burden the team cannot maintain.
How often should custom AI prompts be refreshed?
Weekly is usually the best cadence for active GEO work. Monthly can work for broad monitoring. Daily checks are worth considering only for launches, incidents, high-stakes competitive categories, or prompts tied to fast-moving news.
Should custom prompts come from keywords?
Keywords are useful inputs, but the final prompt should sound like a buyer asking an assistant for help. Use sales calls, support tickets, Search Console queries, competitor questions, and website chat logs to turn keyword themes into natural-language prompts.
How does ReachLLM use custom prompts?
ReachLLM helps teams define buyer-intent prompts, run them across enabled AI platforms, review mentions, competitors, citations, sentiment, raw responses, and Share of Voice, then turn the findings into GEO audits, content, page updates, schema, llms.txt, PR outreach, and follow-up measurement.
Sources
- Ahrefs: How to set up custom prompts to track brand visibility in AI assistants
- Ahrefs: What is Brand Radar, and how to use it?
- Google Search Central: AI features and your website
- Google Search Central: Creating helpful, reliable, people-first content
- OpenAI: Introducing ChatGPT search
- ReachLLM Platform