# AI prompt tracking for buyer questions.

Canonical: https://llmcitely.com/features/prompt-tracking
Publisher: LLM Citely
Reviewed: 2026-10-29

Organize the questions a buyer might ask, approve a baseline and keep the wording attached to every observed answer.

## Track a buyer task, not a collection of clever prompts

AI search monitoring is a different task from debugging prompts inside an application. Start with discovery, comparison, evaluation and adoption questions a prospective customer could use. Save a reason for including each question.

The [question builder](https://llmcitely.com/tools/geo-prompt-generator) provides editable drafts. Its suggestions are not logged customer prompts or search-volume data; a human still needs to review the audience and wording.

## Separate discovery from brand evaluation

A non-branded question can reveal whether a brand appears without being named in the input. A branded question is useful for understanding descriptions, limitations and alternatives. Keep those populations separate in reporting.

For an illustrative analytics client, “Which analytics products suit a two-person SaaS team?” and “Is Northstar suitable for a two-person SaaS team?” answer different questions. Do not blend their mention rates without explaining the mix.

## Preserve the inputs needed for a recheck

Keep the question text, topic, intent, market, language and approved aliases. Confirm the available collection surface in your workspace. A prompt edit is a baseline change, even when the new wording looks similar.

[Choose buyer prompts](https://llmcitely.com/guides/choose-buyer-prompts) before initiating a campaign. Add new questions for a documented reason rather than increasing the count to make a dashboard look complete.

## Review answers before accepting a conclusion

A question row is the starting point for evidence. Inspect the collected answer, cited pages and missing or failed runs. A successful collection without a brand mention is different from a failed request.

Use the [ChatGPT tracking walkthrough](https://llmcitely.com/guides/how-to-track-brand-in-chatgpt) to keep context, observed results and follow-up work aligned.

## Keep the baseline useful for the client

Review whether the approved questions still reflect actual buyer decisions. Archive obsolete questions with their history rather than rewriting the past. When the baseline changes, begin a clearly labeled comparison period.

The purpose is a repeatable observation your team can explain, not an estimate of all ChatGPT users or a claim that every relevant question has been discovered.

## Continue with the next step

- [Track ChatGPT brand mentions](https://llmcitely.com/guides/how-to-track-brand-in-chatgpt)
- [Review the resulting observations](https://llmcitely.com/features/ai-visibility-tracking)
- [Select a defensible baseline](https://llmcitely.com/guides/choose-buyer-prompts)
- [Draft buyer questions](https://llmcitely.com/tools/geo-prompt-generator)
- [Approve a first question baseline](https://llmcitely.com/docs/quickstart)
- [Understand run states](https://llmcitely.com/docs/collection-status-and-retries)

## Your next step

[Build a question set](https://llmcitely.com/tools/geo-prompt-generator)