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Use ChatGPT for Latent Semantic Indexing (LSI) keywords

How to use LSI keywords in ChatGPT

Want better Google rankings without stuffing keywords? Use ChatGPT to map the language of your topic — the result reads like an expert wrote it and signals relevance to search engines.

Search engine algorithms have learned to read between the lines. They no longer simply tally up words; they tune into context, relationships, and meaning. This post shows you, step by step, how to use ChatGPT to create content that speaks the language of your audience and the signals Google expects — the modern practical equivalent of Latent Semantic Indexing (LSI), without the math class hangover.

This is a hands-on guide for content creators, SEOs, and anyone who writes for the web. You’ll get prompts you can paste, a workflow for clustering and validation, and writing tips so your pages feel human and authoritative.

Table of Contents

What semantic indexing is and why it matters.

Think of your article as a small world. If you only give Google one word — the single target keyword — you’ve built a one-room house. When you include related concepts, common questions, opposing viewpoints, and useful examples, you build a neighborhood. Search engines prefer neighborhoods.

LSI became a shorthand for this neighborhood idea. The technical LSI algorithm belongs to academic history; today’s engines use embeddings, BERT, and other neural models, but the practical aim stays the same: cover the topic thoroughly, naturally, and from the perspectives searchers expect.

What to aim for:

  • Clear intent alignment: your title, intro, and meta describe what the searcher wants.
  • Topical breadth: subtopics, FAQs, examples, and comparisons that show you understand the subject.
  • Natural language: sentences that humans enjoy reading, not a laundry list of keywords.

I created a video about how to use Latent Semantic Indexing with the help of ChatGPT, you’ll get the key concepts in just under 5 minutes:

The workflow: from a seed idea to a semantic-rich post

I’ve broken this into repeatable steps you can apply in 20–60 minutes, plus optional advanced steps.

1. Start with a precise seed and intent

Seed example: best espresso machine for home

Specify intent to ChatGPT: are you writing a buying guide, a how-to, a comparison, or a review? Intent drives the shape of the article. If someone searches “best espresso machine for home,” they’re likely in buying mode — give them comparisons, price ranges, and quick recommendations.

Prompt to start:

Act as an SEO content strategist. For the seed keyword "best espresso machine for home", list 50 related search phrases, grouped by user intent: (a) research, (b) buying, (c) maintenance, (d) comparison, (e) accessories. Return as JSON with arrays for each intent.

This generates a map rather than a list — you’ll quickly see natural clusters.

2. Cluster those phrases into an outline

Ask ChatGPT to group the terms into 5–7 subtopics that would form your H2s and H3s. Ask for three FAQ questions per subtopic to populate a FAQ schema later.

Prompt to cluster:

Using the phrase groups, create a blog outline with H2s and H3s. For each H2, suggest 2–3 supporting H3s and 3 FAQ questions. Keep the headings search-friendly and human.

You’ll get an outline that already looks like a publishable structure.

3. Verify and filter with data

Never publish straight from the model. Use one quick check: plug the highest-potential phrases into Google to see who ranks and what format they use. Validate search intent with:

  • Top 5 SERP titles and snippets — do they match buying intent? informational? listicles? learn more about SERP and intent here.
  • People Also Ask (PAA) — which questions appear repeatedly?
  • Tool check (optional) — Ahrefs, SEMrush, or GSC for volume and difficulty.

Discard or demote suggestions that don’t match intent or look like low-value noise.

4. Write using clusters, not keywords

Draft sections using the outline. When you need to insert related phrases, do it where they fit — in headings, explanations, or example sentences. Keep sentences varied. Prefer clarity and rhythm over forced keyword density.

Example micro-prompt:

Rewrite this paragraph to include the phrases "espresso machine maintenance", "descaling frequency", and "water hardness" naturally, keeping a conversational tone.

5. Add structured material and schema

Include an FAQ section with the model-generated questions and concise answers. Use lists, pros/cons, and comparison tables where helpful. These elements increase chances of rich results.

6. Publish and iterate

Monitor clicks, rankings, and user behaviour. If traffic is the wrong intent, tweak the title and meta to better match what people expect. If rankings stall, expand with more subtopics or authoritative examples.

Tried-and-tested prompt bank (copy-paste ready)

Use these prompts to get consistent, high-quality output from ChatGPT.

  • Seed expansion:
Act as an SEO research assistant. For the keyword "[seed]", produce 50 related search phrases grouped by user intent: research, buying, how-to, comparison, and problems. Output as JSON.
  • Outline builder:
Create a detailed article outline from this list. Include H2s and H3s, 2–3 bullet points per subsection, and three FAQs for schema. Keep it concise and user-focused.
  • Natural rewrite:
Rewrite this text to include these phrases naturally: [list]. Keep tone conversational and flowy; no robotic lists.
  • Meta helper:
Suggest three meta titles and three meta descriptions (under 160 characters) that match buying intent for "[seed]".

Advanced: embeddings and data-driven clustering

If you want a sturdier, more objective map, turn your list of phrases into numerical representations (called embeddings) and then group similar ones together using simple clustering methods, such as K-means or HDBSCAN. In plain terms, this means the computer looks at which phrases are closely related in meaning and places them in the same bucket. This moves you from model intuition to vector-space evidence: phrases that sit near each other in embedding space truly belong to the same semantic neighborhood.

This is optional, and it requires API access to embeddings or a tool that supports them. The benefit? Your H2s reflect real semantic distances rather than pattern recognition alone.

Writing tips so the page reads like a human wrote it

  • Vary sentence length. Mix short, punchy lines with longer, illustrative sentences.
  • Use real examples and tiny stories. A single concrete detail beats a paragraph of vague authority.
  • Avoid passive, bureaucratic language. Read the paragraph out loud: if it sounds flat, rewrite it.
  • When in doubt, delete. Cleaner text signals expertise.

Example section (mini case study)

Seed: “best espresso machine for home”

Subtopics from ChatGPT:

  • Quick recommendations (budget, mid-range, premium)
  • Buying checklist (capacity, boiler type, grinder, water hardness)
  • Maintenance basics (descaling, seals, backflushing)
  • Troubleshooting (weak shots, pump noise, temperature issues)
  • Accessories and upgrades

A short, human paragraph under “Maintenance basics” might read like this:

Descaling is the gentle hygiene of an espresso machine — skip it, and bitter scale will creep into your water pathway. The cadence depends on your water hardness: soft water, every 3–4 months; hard water, every 4–6 weeks. Simple tools (a quality descaler and a portafilter brush) will keep extraction crisp and the steam wand singing.

That kind of detail tells both the reader and the algorithm that you understand the topic.

Publishing checklist for WordPress

  • ✅ Use the target phrase in the title and once in the first 100 words.
  • ✅ Use 1–2 related phrases naturally in headings.
  • ✅ Add an FAQ block and mark it with schema (Yoast, Rank Math, or manual JSON-LD).
  • ✅ Add an internal link to a related post and 1–2 authoritative external links.
  • ✅ Set a featured image and write a descriptive alt text.
  • ✅ Write a meta description that matches intent and includes the primary phrase.

Final note — the human touch matters

AI accelerates the creative work, but it doesn’t replace judgment. Use ChatGPT to unearth the language of your topic, then choose the phrasing that sounds right for your audience. When your content reads like a human wrote it — with curiosity, clarity, and a dash of personality — both people and search engines respond.

I hope this helps you in your AI journey. Feel free to check out the courses page for more information on how LSI keywords fit in the overall ChatGPT for marketing journey.