An AI visibility dashboard is only as useful as the questions inside it.
If you track broad phrases that nobody would ask while choosing a product, you can collect a neat chart without learning much about your business. The harder—and more useful—work is deciding which buying situations deserve a place in your prompt set.
Disclosure: I’m Zia, co-founder of Depra AI. This is the approach I suggest for building a practical monitoring set; the examples below are illustrative, not measured results.
Start with a buying situation
“Skincare” is a topic. “Which fragrance-free moisturiser should I choose for sensitive skin in India?” is a decision.
The second question gives an assistant something to work with: a product category, a constraint, a location and a reason for choosing. It also gives your team a useful way to interpret the answer. Did the assistant mention your brand? Did it understand your product? Which alternatives and sources appeared?
Use customer conversations, support tickets you’re permitted to use, sales questions and your existing search research to identify these situations. Keep personal customer details out of the prompts. You need the buying problem, not the customer’s identity.
Build a small matrix before adding prompts
For each product or service, write down:
- The buyer: who is making the decision?
- The job: what are they trying to achieve?
- The constraint: budget, geography, compatibility or a product requirement.
- The stage: discovering options, comparing a shortlist or checking a specific concern.
- The language: how would this buyer naturally ask?
For an Indian SaaS business, those combinations might produce questions such as “Which AI visibility tools can a small marketing team use to track brand recommendations?” and “What should I compare before choosing an AI visibility tool for an Indian brand?”
Avoid building the entire set from questions that already contain your brand name. Branded questions help you check accuracy and perception. Non-branded questions help you see whether you enter the shortlist at all. Both deserve attention, but they answer different business questions.
Treat suggested prompts as drafts
A prompt discovery tool can help you move from topics to candidate questions. Review those candidates before tracking them.
Would a customer actually ask this? Does the question describe your category clearly? Is it so broad that the answer could refer to a different type of product? Does it contain an assumption that steers the assistant toward your brand?
Search demand can help prioritise topics, but a keyword volume estimate should not be presented as the exact number of people asking that question in an AI assistant. Label the signal for what it measures.
Test English and Hinglish as separate questions
If your customers use Hinglish, include it deliberately. Preserve the buying intent rather than producing a word-for-word translation that sounds unnatural.
For example:
English: “Which AI visibility tools support English and Hinglish prompts for Indian brands?” Hinglish: “Indian brands ke liye English aur Hinglish prompts track karne wale AI visibility tools kaun se hain?”
Review both versions with someone who understands the audience. Keep the language versions identifiable in your reporting. A difference between two answers is a reason to investigate; one pair of responses does not establish a lasting language effect.
Keep a stable core
Once you have a useful set, keep a core of prompts unchanged across reporting periods. Record the prompt text, language, engine, collection date and relevant settings.
If you change the questions every week, a change in visibility may reflect a change in the sample. Add exploratory prompts separately and record when you introduce them.
Read the answers behind the score
A mention, a citation and a website visit are different events. An assistant can name a brand without linking to its website. It can cite a page without recommending that brand. A visit requires separate traffic measurement.
Inspect the collected answers and source links before deciding what to change. Look for inaccurate product facts, unanswered buyer concerns and useful pages your own site is missing. Then choose one concrete action and record it.
In Depra AI, the workflow brings tracked prompts, collected answers, source analysis and suggested actions together. It supports monitoring across ChatGPT, Gemini, Google AI Overviews and Perplexity, including English and Hinglish questions. Collection methods and schedules matter when interpreting any result; consult the current methodology and plan details at depra.ai.
A useful first step
Write ten questions covering your most important buying situations. Include a mix of discovery, comparison and accuracy checks. Review their wording, keep the core stable, and use the actual answers to decide what to improve.
The goal is a monitoring set you can explain—and a next action you can defend.
Learn more about Depra AI, or read how to measure AI visibility and how to write Hinglish tracking prompts.