Create a Topical Map using AI

Throwing more blog posts at the wall rarely sticks. Search engines now reward sites that demonstrate true topical authority, not random keyword wins. A topical map—the blueprint of every entity + attribute your audience cares about—solves that, but manual mapping is slow, error-prone, and rarely deep enough. Moonlit’s “Generate Topical Map” app flips that script by letting an LLM build and expand a 100-topic cluster (plus 10 future clusters) in minutes. For instance, if you’re looking to automate your content optimization, this tool sets the stage for scalable strategies.

How the app is built

1. Inputs that frame the strategy

The user drops in six plain-text fields: website name, source context, central entity, central search intent, and their starter ideas for the core (monetized) and outer (informational) sections. These act as guardrails—keeping the AI on-brand and on-intent. You can read more about maintaining a consistent brand voice across thousands of pages to ensure your message stays uniform.

2. Instant background research (Chat Model – Perplexity)

The first Chat Model hits Perplexity Sonar-Pro with the central entity alone. Why? Perplexity is cost-efficient for rapid fact gathering. The node returns a concise knowledge burst—stats, synonyms, related concepts—that gives GPT-4o richer context without bloating the prompt.

3. Drafting the 100-topic map (Chat Model – GPT-4o)

The second Chat Model feeds GPT-4o a synthesized prompt:

• Merges the six user inputs with the Perplexity research.

• Includes a detailed system message that teaches the model what a topical map is, how to split monetized vs. informational topics, and formatting rules (markdown, star the root, exactly 50 ideas per section).

• The low temperature (model defaults to 0.2) keeps lists laser-focused and de-duplicates overlapping topics.

Output: a markdown file with 50 core topics and 50 outer topics—each “Entity + Attribute” pairing ready to become a page, pillar, or product listing. This approach is similar in principle to content automation strategies designed to rapidly scale production while keeping quality intact.

4. Finding new clusters to tackle next (Chat Model – GPT-4o)

The final node reads the finished map, then brainstorms 10 entirely new central entities (e.g., if the first map is about “Espresso,” the expansion might cover “Cold Brew,” “Latte Art,” etc.). Each idea shows:

• A short search intent sentence.

• 3–5 monetizable core ideas.

• 3–5 supporting outer ideas.

This turns one content cluster into a full editorial roadmap.

Ways to customize