The Center for Campaign Innovation’s most recent post-election survey found that 11% of voters used an artificial intelligence tool like ChatGPT to get information about political candidates or the election. Additional research from Gartner predicts a 25% drop in traditional search engine use by 2026.
AI generated responses about candidates, elections, and policy issues are emerging as a new digital battleground for campaigners. Large language models (LLMs) are the technology powering AI tools. Getting into those answers requires a different playbook from traditional search engine optimization (SEO). In this article, you’ll learn how large language model optimization (LLMO) differs from SEO.
How SEO Works vs. How LLMO Works
SEO strategy focuses on creating content around specific “keywords,” which are phrases that a user is searching for with sites like Google or Bing, and then gaining authority in the form of links from other websites (called “backlinks”). Search engines are constantly crawling the internet to update their search results.
LLMs, however, are built from training snapshots where the AI catches up periodically. So, while a new blog post can rank on Google tomorrow, an AI chatbot may not “learn” about it for months unless it uses real-time search.
The Goal of LLMO
With SEO, the objective is driving traffic to your website. LLM Optimization, on the other hand is about simply getting your candidate or policies mentioned inside an AI’s reply. This leads to a winner-take-all competition.
With search engine results, it’s sufficient to be on the first page, but with AI, only a handful of sources the model chooses make the answer. If your content isn’t cited, you’re invisible.
Content Depth and Length
The long, exhaustive posts needed for SEO still help—depth proves authority and supplies training material—but LLMs only surface the essential takeaways. Make sure written content has a concise summary, clear subheadings, or FAQ boxes so the AI model can lift the core insight without wading through fluff.
Structure and Readability
Formatted text is easier for humans and AIs to parse. Use logical H2/H3 headings, bullet lists, numbered steps, and descriptive alt text for images. Avoid hiding key points in images – LLMs mostly learn from plain HTML text.
Tone and Style
Chatbots answer in a conversational voice. Content that already sounds like a helpful explanation (short sentences, minimal jargon, active voice) is more likely to be reproduced faithfully. Overly formal or comms-heavy language tends to be paraphrased—or ignored.
Conclusion
Campaigners need a dual-track content creation strategy that includes both pages that rank highly on Google and passages that an AI can quote instantly. This means keeping technical SEO tactics in place while layering on AI-friendly practices like clear answers, structured layouts, and a conversational tone.
As voters shift to using AI chatbots for their electoral research, it’s essential that campaigns take their message there as well.
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