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Hi everyone πŸ‘‹ Luis & Rui here with an AI-powered SEO deep dive.

Want to build a bulletproof keyword research pipeline with the help of AI?

In this issue, we’ll show you how to leverage Perplexity AI combined with ChatGPT to generate rich, intent-driven keyword lists in minutes and export them into a ready-to-upload CSV for Google Sheets.

Let’s go!


🧰 What You Need

  • Perplexity account (free tier is enough)

  • ChatGPT account (for CSV generation)

  • Google Sheets (or Excel)


Here’s the workflow you’ll follow before diving into each prompt:

  1. Run each prompt in the same Perplexity conversation to pull rich data for primary, long-tail, voice-style, FAQ, and semantic terms.

  2. Copy all outputs from Perplexity once you’ve completed prompts 1–5.

  3. Paste everything into ChatGPT with our CSV wrapper prompt to automatically generate a structured download.

  4. Import the CSV into Google Sheets or Excel for filtering, sorting, and review.

  5. Pick any high-value term and re-run prompts 2–5 in Perplexity for deeper cluster expansion.


1. Main Topic Keywords πŸ”‘

Prompt:
Generate a prioritized list of the 25–30 highest-volume primary keywords for INSERT_TOPIC, including for each entry:

  1. Estimated monthly search volume (globally and by major English-speaking region)

  2. Average cost per click (CPC) on Google Ads

  3. Broad competition score (Low/Med/High)

  4. Primary user intent (Informational/Transactional/Navigational)

  5. Suggested URL or content type that typically ranks in position 1

Sort by descending search volume and highlight any keywords with CPC above $2.00.


2. Long-Tail & Modifier Keywords πŸ“

Prompt:
Identify 30–50 long-tail keyword phrases (4+ words) closely tied to INSERT_TOPIC, and for each:

  • Monthly search volume

  • Modifier type (e.g., β€œhow to,” β€œbest,” location-based, problem-solution)

  • Difficulty estimate (Low/Med/High)

  • Geographic or seasonal modifiers (e.g., β€œ2025,” city names)

  • Cluster label by modifier type (e.g., β€œHow to…” vs β€œBest …”)


3. Conversational (NLP) Queries πŸ’¬


Read more

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