How to Optimize Content for ChatGPT and Large Language Models (LLMs)

The way people find answers online is undergoing its biggest shift since the dawn of search engines. Where once a Google query returned a tidy list of “blue links,” today’s users are increasingly seeing AI-generated summaries, conversational responses, and curated answers from tools like ChatGPT, Google Gemini, and Perplexity. Gartner even forecasts that by 2026, traditional search volume will decline by 25% due to the rise of generative AI agents.

This is more than a new interface—it represents a profound change in how content is discovered, referenced, and consumed. The question businesses now face is clear: How do we ensure our content gets seen and cited by these new AI systems?

The discipline of LLM optimization is emerging as the answer. It builds upon familiar practices like SEO, while incorporating new strategies to make content both AI-readable and AI-credible. In this expanded guide, we’ll explore how you can optimize content for ChatGPT and other LLMs, what strategies matter most, and why Contently makes an indispensable partner for brands navigating this transition.

1. From Search Engines to Answer Engines

When Google launched in 1998, its innovation was simple: rank pages by relevance and authority using PageRank. Content marketers and SEO professionals built entire industries around optimizing for that model.

But with the rise of generative AI, that model is evolving. Tools like ChatGPT, Claude, and Gemini aren’t simply ranking existing pages—they’re synthesizing answers by pulling from massive training data, licensed sources, and live web integrations.

This has led to new terminology in the field:

• Answer Engine Optimization (AEO): Crafting content that can be directly quoted or repurposed in AI-generated answers. AEO favors Q&A formats, structured data, and direct, clear explanations.
• Generative Engine Optimization (GEO): Ensuring content surfaces in “AI Overviews” and other generative summaries. GEO requires semantic depth, authority, and optimized topical coverage, as seen in Semrush’s cohort study and tools catalogued by NoGood.
• LLM Optimization (LLMO): A broader practice that combines elements of SEO, AEO, and GEO. It focuses on making content legible, trustworthy, and easy to integrate into AI responses.

The lesson? Search is no longer just about ranking high—it’s about being referenced by AI systems that summarize knowledge for the user.

2. How LLMs Reference and Prioritize Content

To optimize for LLMs, you first need to understand how they select and present content. Unlike search engines, which display multiple options, LLMs typically deliver a single synthesized answer—often citing only a handful of sources.

Here’s how that process works:

1. Training and Fine-Tuning: LLMs are initially trained on huge datasets. While most of this data is broad and historical, they’re increasingly supplemented with licensed content partnerships (e.g., OpenAI’s deals with publishers).
2. Retrieval-Augmented Generation (RAG): Many AI systems now pull fresh data at runtime. For example, Perplexity AI combines its LLM with live web crawling, allowing it to cite sources in real-time.
3. Authority and Semantic Fit: Models are more likely to surface content that demonstrates E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). Technical formatting, clear citations, and recognized authority increase the odds of being selected.
4. Clarity and Accessibility: LLMs favor text that is structured, explicit, and unambiguous. A buried statistic in the middle of a long, jargon-heavy paragraph is less likely to be cited than a cleanly stated fact at the beginning of a section.

The implications are clear: your content must be not only accurate and useful but also machine-friendly.

3. Practical Tactics for Optimizing Content for ChatGPT and LLMs

a) Structure for Machines and Humans Alike

• Use descriptive headers (H1–H3) with natural language questions (“What is GEO?”).
• Add FAQ sections that directly answer common queries.
• Mark up with schema (FAQ, How-To, Article) so both search engines and AI systems can parse intent.

b) Lead with the Answer

Models often extract the first clear, factual statement they encounter. For example, instead of writing:

“While brands often experiment with a variety of GEO tools, one worth mentioning is Otterly’s GEO Audit, launched in April 2025.”

Write:

“Otterly launched GEO Audit in April 2025, making it one of the first dedicated GEO assessment platforms.”

This makes it far easier for an LLM to lift your answer verbatim.

c) Semantic Depth Over Keywords

Traditional SEO often encouraged “keyword density.” LLM optimization values semantic breadth. Cover all dimensions of a topic: definitions, use cases, pros/cons, and related concepts.

For example, an article on Profound’s $20M funding round should also mention AthenaHQ and Scrunch AI to provide context. This makes your piece more attractive for summarization engines.

d) Build Authority Through Sources

LLMs are risk-averse: they’d rather cite a well-documented, externally backed claim than a bare assertion. Support your content with external citations—ideally primary sources like McKinsey’s global AI adoption report, Semrush’s AI Overviews data, or respected media outlets like the Wall Street Journal.

e) Keep It Fresh

LLMs integrate recency signals. Refresh old articles with updated statistics, case studies, and links. Peec AI’s €7M funding in July 2025 is a perfect example—new, relevant information that makes your content more attractive for current AI outputs.

f) Ensure Technical Accessibility

• Don’t block crawlers (robots.txt).
• Use clean HTML.
• Adopt llms.txt files (where supported), which indicate permission for AI models to ingest your content.

g) Track AI Exposure

Monitor mentions of your content in AI systems. While still emerging, tools like MarketMuse and RankScale (covered in Writesonic’s roundup) are beginning to integrate “AI citation visibility” metrics.

4. Case Studies: Who’s Winning the GEO/LLMO Game?

• MarketMuse & Monday.com: By restructuring their blog around comprehensive topical clusters, MarketMuse helped Monday.com boost organic traffic by 1,570%. This not only improved SEO performance but also made their content more likely to appear in AI answers.
• Otterly AI’s GEO Audit (April 2025): Otterly launched a specialized audit tool to measure how “AI-citable” content is. This reflects the growing demand for metrics beyond rankings—focusing on AI presence.
• Profound’s Series A ($20M, June 2025): Profound is betting its platform will become indispensable as brands pivot from “blue links” to AI-driven visibility. Its growth mirrors the overall GEO ecosystem, which now includes startups like AthenaHQ and Scrunch AI.

These examples prove that LLM optimization is no longer theoretical—it’s a competitive frontier where brands are investing real money.

5. The Role of Contently: Why Partnership Matters

Scaling content that is both human-engaging and machine-readable is not trivial. It requires consistent application of formatting, authority, and narrative clarity. This is where Contently stands out.

With its Creative Marketplace of 160,000+ vetted freelancers, Contently can:

• Produce high-quality, structured content that balances depth, clarity, and narrative flow.
• Ensure each piece adheres to AEO/GEO/LLMO principles, embedding Q&A sections, schema, and authoritative sourcing.
• Deliver ongoing content refreshes so your brand maintains visibility in a fast-changing AI ecosystem.
• Provide editorial oversight so articles retain human warmth and brand voice while still being machine-friendly.

In an era where generative AI increasingly “decides” what information users see, quality and structure are no longer optional—they’re survival tools. Contently’s dual expertise in editorial excellence and scalable production makes it an ideal partner for brands seeking long-term AI visibility.

6. The Road Ahead: What Comes After SEO?

SEO is not disappearing. Google’s index remains massive—26.7B keywords tracked by Semrush as of 2025—and organic rankings will continue to matter.

But the future is blended:

• AI Summaries will sit above or alongside traditional results.
• Voice interfaces (Siri, Alexa, Gemini) will rely almost entirely on generative answers.
• Enterprise RAG systems will curate internal and external content, rewarding companies whose material is clear, trusted, and structured.

In this environment, LLM optimization is not a side project—it’s the new SEO. Brands that adapt early will secure a durable advantage in how customers discover and trust their expertise.

Conclusion

The world of online discovery is evolving faster than at any point since the rise of search engines. By 2026, a quarter of searches may never happen in Google at all, according to Gartner. Instead, they’ll happen in AI assistants that summarize the web in a single response.

To thrive in this new world, content must be structured, authoritative, fresh, and accessible. It must be written not just for humans, but also for the machines that increasingly decide which human voices get heard.

That’s why LLM optimization—anchored by a partner like Contently—isn’t optional. It’s the next chapter of digital strategy. Those who adapt will be the ones AI cites, credits, and amplifies. Those who don’t will fade into obscurity, unseen in both search results and AI-generated answers.