LLM Content Strategy: A 5-Step Playbook

Want a simple system to plan, draft, and ship AI-ready content? This 5-step playbook helps you scale output, keep quality high, and win AI Overviews.

Grab the toolkit: Download the 5-step checklist (PDF) and the GEO/LLMO scorecard.

What is an LLM Content Strategy?

It’s a repeatable system to research, write, edit, and optimize content with Large Language Models—and for them.

Think planning with data, drafting with AI, editing with humans, and formatting for LLM visibility. See the overview from Wellows and practical workflow tips from Techmagnate.

What outcomes should you expect in 90 days?

• 2x output without adding headcount
• Higher accuracy via human review and citations
• Better LLM visibility (GEO/LLMO) and AI Overview wins
• Faster time-to-publish and stronger topic coverage

How does the 5-step playbook work?

Follow five steps: Research, Structure, Draft, Edit, and Optimize/Distribute. Each step includes a checklist, prompt starters, and GEO moves.

Step 1: How do you pick topics and keywords with LLMs?

Use AI to find demand, but ground choices in real data and SERP analysis.

Do this

• Map intent and topic clusters (semantic SEO) using search data. Pair manual research with LLM clustering; see guidance from Wellows.
• Focus on mid-to-bottom funnel where prompts and queries overlap, per Growfusely.
• Refresh existing pages first; LLMs prioritize recent, well-structured sources, per Adobe and Fibr AI.
• Collect related concepts for semantic completeness (entities, synonyms, adjacent terms), per Fibr AI.

Mini checklist

• Primary keyword + 3–5 long-tails
• 3 user intents (why they search)
• Competitor gaps and citation gaps
• Entities to include (people, tools, metrics)

Prompt starter

"Cluster these keywords by intent and SERP similarity. Recommend 10 post ideas with one job-to-be-done each, plus key entities and FAQs."

Step 2: How do you structure content for LLMs?

Make your content easy to parse: clear headings, short blocks, Q&A, and explicit summaries.

Do this

• Use a clean heading hierarchy (H2/H3) and short paragraphs, per Flow Agency.
• Include question-based H2/H3 and direct answers; AI cites Q&A more often, per Averi.
• Add a “Key Takeaways” block after each section; consistent chunks help embeddings, per Promodo.
• Implement schema thoughtfully (Article, FAQ). Keep critical info in HTML, not only JSON-LD, per Averi and ABA.

Formatting checklist

• One idea per paragraph
• Bullets for lists and steps
• FAQ at the bottom
• Plain language; explain any jargon

Key Takeaways: Answer the question first, then details. Use short blocks. Add Q&A and citations. Update often.

Step 3: How do you draft fast without losing quality?

Draft with an LLM, but control inputs, structure, and review. You stay in charge.

LLM drafting routine

• Feed the brief: target keyword, audience, outline, tone, must-include entities.
• Ask for an outline, then refine sections one-by-one to reduce drift.
• Request 6th-grade reading level and concrete examples.
• Insert source prompts (what to cite, preferred links).
• Run a self-critique pass to catch gaps, as advised by MarketMuse.

Prompt skeleton

"Write a section that answers the H2 in 100–150 words, then add 3 bullets and a 2-sentence takeaway."

"List 5 credible sources to cite for this claim, preferring .gov, .edu, and news."

"Rewrite for clarity at a 6th-grade level. Keep key terms: LLMO, GEO, structured data."

Step 4: How do you keep accuracy high?

Use human-in-the-loop editing and cite reputable sources. Verify facts before you publish.

Editor pass (human)

• Check facts and dates; add citations and links.
• Trim fluff; keep one idea per block (helps LLM chunking), per Promodo.
• Add summaries and FAQs; LLMs favor direct answers, per Averi.
• Refresh 10–15% of content regularly; LLMs weight freshness, per Adobe.

Advanced aid

• Use summarization to condense sources and compare versions, per Galileo.
• Record revision notes so future updates are fast (freshness signal).

Step 5: How do you optimize for AI Overviews and LLMs (GEO/LLMO)?

Structure pages for machine parsing, add schema, publish ungated detail, and build citations.

On-page GEO moves

• Schema: Article + FAQ; keep key facts in HTML. See cautions from Averi and tips from ABA.
• Q&A blocks with natural questions (voice-like), per Flow Agency.
• Freshness: update stats, examples, and screenshots; use near-real-time data when relevant, per Fibr AI.
• Unguarded depth: publish detailed, readable content, per Growfusely.

Earned mentions

• Get cited across diverse, authoritative domains; track source mix, per Nick Lafferty.
• Publish original data, get quoted, seed insights on forums; ideas from this thread.

GEO/LLMO scorecard (use before publish)

Area	What to check	Target
Structure	H2/H3, bullets, Q&A, takeaways	Pass
Schema	Article + FAQ in HTML and JSON-LD	Present
Freshness	10–15% updated; current stats	Updated
Citations	Diverse, authoritative sources	4+
Readability	6th-grade, short sentences	Meets

Standard operating procedures (SOP) and prompt library

SOP highlights

• One job-to-be-done per article
• Outline → section-by-section draft → human edit → GEO scorecard
• Add FAQ, schema, citations, and key takeaways

Copy-paste prompt starters

"Given [audience] and [keyword], propose an outline with Q&A H2s and must-have entities."

"Draft this H2 in 120 words, add 3 bullets and a 1-sentence takeaway."

"List 5 credible sources for verification and 3 internal links to add."

"Rewrite for clarity and GEO: short blocks, direct answers, and a FAQ."

Implementation timeline (30/60/90)

Days 1–30

• Audit top 20 URLs with the scorecard
• Ship 4 refreshed posts with Q&A, schema, citations
• Build topic cluster plan (10 posts)

Days 31–60

• Publish 6–8 net-new posts using the SOP
• Start earned citation outreach; pitch 2 original data angles
• Add tracking for LLM mentions and AI Overviews, per Nick Lafferty

Days 61–90

• Double down on winners (refresh, expand FAQs)
• Standardize prompts and templates across the team
• Create a monthly freshness calendar (10–15% updates), per Adobe

Metrics that matter

• Content velocity: drafts → published per week
• Refresh rate: % pages updated monthly
• Coverage: entities/FAQs included per cluster
• LLM visibility: citations, AI Overview appearances, source diversity
• Quality: editor rework time, factual corrections

FAQ

What is the difference between GEO, LLMO, and SEO?

GEO/LLMO focuses on getting cited by LLMs and AI Overviews, while SEO targets traditional rankings. They overlap; see Flow Agency.

Should we still use keywords?

Yes—naturally and clearly. Keep structure minimal and readable, per Growfusely.

Do we need schema markup?

Schema helps, but put critical info in the HTML. Some AI agents may skip JS-injected JSON-LD; see Averi and ABA.

How often should we update content?

Refresh 10–15% regularly. LLMs and search engines weight freshness, per Adobe and Fibr AI.

How do we measure LLM visibility?

Use tracking tools and monitor citation source diversity; see Nick Lafferty.

Next step

Use the checklist on your next post. Keep the structure tight, update often, and track LLM citations. Then scale the workflow to every page.