AI & SEO – How LLMs Read in Between the Lines of Search Queries
The way people search is changing. Traditional search engines rely on short keyword strings, but with AI-driven search tools powered by Large Language Models (LLMs) like ChatGPT, people are starting to use longer, conversational queries.
The big difference? LLMs don’t just take what you say literally. They also unpack what you mean.
From Keywords to Meaning
When you type ‘LED strip lights for kitchen’ into Google, the algorithm looks for those exact keywords on websites and serves up pages that match.
However, when using an LLM like ChatGPT, Gemini, or Copilot, queries are written in natural language; for example, with a query like ‘Recommend an energy-efficient COB LED light strip that I can install in my kitchen,’ an LLM will interpret it much more comprehensively.
- Energy-efficient = must have a low wattage per metre, be inexpensive to run.
- COB = chip-on-board LED, the product must be of high quality to provide a brighter and more uniform light.
- I can install = the product recommended must be suitable for DIY and very easy to install.
- Kitchen = needs water resistance (IP65 or higher, to handle splashes and to be dust resistant) and also needs bright, white light (higher Kelvin rating, something like 6000K).
In other words, the model is trained to infer context and intent from your words, not just match them.
The Role of ‘Grounding’
This ability to make inferences is powerful, but it relies on grounding: connecting the AI’s assumptions to structured, reliable data. If your website or product listing includes detailed specs like ‘water-resistant IP65’ or ‘Kelvin: 5000K, bright white’, then the AI has what it needs to confidently ground its answer.
If not? Your business might get skipped over in favour of a competitor who has provided that information.
Why This Matters for SEO
To be recommended by AI search, your content needs to do more than rank for keywords. It needs to provide the contextual clues that LLMs look for when filling in the gaps of a user’s natural language query.
Here’s how you can prepare:
- Speak about Use Cases
Don’t just list your product name. Spell out how and where it can be used. (‘Ideal for kitchen splash backs, outdoor patios, and bathrooms.’) - Provide Enhanced Data
Include structured information, brightness, watts per metre, warranty, installation guides, IP rating, etc. This allows LLMs to ‘ground’ their inferences in facts. - Repeat Across the Web
Make sure the same rich descriptions appear on your website, social media, product listings, forums, and directories. Consistency increases the likelihood that AI tools will surface your brand.
The Bigger Picture
The shift to AI search isn’t about abandoning traditional SEO, but layering in a new dimension: optimising for how LLMs think. They’re not just matching words; they’re interpreting meaning, context, and innuendo. The businesses that succeed will be those that anticipate what AI models infer, and then provide the structured data to support those assumptions.
Want to know more about AI & SEO?
Watch our free webinar about ‘How to get found in the new age of search’.
Sharing Our Expertise
Related Guides
Our knowledge hub is where we share what we’ve learned to help you stay informed, grow your skills, and make smarter digital decisions with confidence.
