Topical Map: data-validated prompt list

The Topical Map helps you decide which prompts and topics are worth tracking and optimizing — instead of guessing. It builds a hierarchy of pillars → topics → prompts, prioritized by buyer intent and validated against real search data, then feeds the winning prompts into tracking.

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Before you start

The map is only as good as the business context behind it. Make sure your project has:

• A clear description of what the business sells and who its best customers are.
• A short list of competitors and alternatives buyers actually compare you against (3 is a good number).
• Optionally, a Google Search Console connection and any manual keyword lists you want to validate against.

You don’t need all of this to start — but the more context you give, the sharper and more commercial the map will be.

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The four stages

A Topical Map is built in four stages. You can move through them in order, and you can come back and re-run any stage as your context improves.

1. Build — generate the commercial topic hierarchy from your business context.
2. Validate gaps — check the map against competitors for obvious missing topics.
3. Validate with data — confirm topics against real search data and build keyword clusters.
4. Track — push the high-priority prompts into prompt tracking.

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Stage 1 — Build the commercial topical map

The map starts from a commercial context filter: a focused summary of what the business sells, its best customers, the problems they urgently want solved, the use cases that generate revenue, and the competitors buyers compare. This filter is what keeps the map commercial instead of generic. From that context, Amadora AI generates a hierarchy:

• Pillars are written in business language — the categories the brand actually sells (e.g. “AI Search Optimization Platform”, “Agency GEO Service Delivery”).
• Topics under each pillar are written in search language — closer to how buyers actually phrase things (e.g. “AI visibility tracking software”, “LLM SEO software”).
• Prompts are the example queries a buyer would type into an AI engine under each topic.

Each topic carries three labels so you can prioritize quickly:

• Intent — BOFU (bottom of funnel) or MOFU (middle of funnel). High-intent commercial topics are favored over broad awareness topics.
• Topic type — e.g. Product / Category / BOFU, comparison, pricing, use-case.
• Priority — High / Medium / Low.

To review the generated map, open the Tree view. Each pillar expands to show its topics, each topic shows its priority badge and search volume, and each topic expands to its prompts. Click See detail on any topic to open the side panel with its keywords, volume, difficulty, CPC, and example queries.

Tip: Use the Context view to see the exact business context the map was built on — what the brand sells, ideal customers, and competitors. If the context is off, fix it there and re-build; everything downstream depends on it.

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Stage 2 — Validate gaps against competitors

Once the first version exists, this stage checks it for obvious missing commercial topics — without rewriting what you already have. It compares your map against your business model and your competitors’ commercial topics, then answers five questions:

1. Are any obvious commercial pillars missing?
2. Are any obvious BOFU/MOFU topics missing under existing pillars?
3. Are any buyer-intent query patterns missing?
4. Are any competitor-covered commercial topics absent from the map?
5. Are any AI-search or PPC trigger topics missing?

This stage is deliberately conservative: it suggests additions only, it doesn’t remove, downgrade, or restructure your existing topics. Competitors fill gaps — they don’t define the strategy. Review the suggested additions and accept the ones that fit. You can run this stage by clicking Enrich map, which expands the map with missing commercial topics based on your context and competitors.

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Stage 3 — Validate with real-world data

This is where strategic topics become evidence-backed keyword clusters. The map is validated against three data sources:

• Google Search Console — confirms which topics you already get impressions and clicks for.
• Competitor keyword research — confirms which topics competitors rank and bid on.
• Manual keyword lists — your own keywords, if you have them.

For each topic, this stage confirms:

• Does this topic exist in real search data?
• Are people using similar language?
• Is it commercially relevant?
• Can we form a real keyword cluster around it?
• Should it stay High / Medium / Low priority?

The result is visible in two places:

• Topic detail panel — open any topic and see its keyword table with Volume, Difficulty, and CPC per keyword, plus the total volume for the topic and a set of example queries. The Source icon shows where each keyword came from.
  
• Keywords view — every keyword across the whole map, grouped into clusters with a volume total per cluster (e.g. “AI visibility tracking software — 3,330”, “best AI visibility tools — 2,430”). This is the fastest way to size opportunity across the map.
  

Use these numbers to confirm or adjust each topic’s priority before you commit to tracking.

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Stage 4 — Track the winning prompts

The whole point of the map is to decide what to track. Once topics are validated, push the high-priority prompts into prompt tracking so you monitor AI visibility for the queries that were proven to matter. In the Tree view, each pillar and topic shows how many prompts it contains, and the header shows how many are currently tracked. Add prompts to a topic with +Add prompt, add a new topic with +Add topic, and the prompts you select flow into your tracked set. From there, Amadora monitors those prompts across AI engines like ChatGPT, Claude, Perplexity, Gemini, and AI Overviews — the same prompts that your map proved are tied to real buyer demand.

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Exporting the map

Click Export CSV (top right) to download the full map — pillars, topics, prompts, intent, priority, and keyword data — for your own reporting, client decks, or planning.