LLM Optimisation: Getting AI Models to Recommend Your Brand
LLM Optimisation is the practice of improving how Large Language Models (LLMs) perceive, reference, and recommend your brand in their generated responses.
Large Language Models power the AI assistants that hundreds of millions of people now use to research products, compare services, and make purchasing decisions. When someone asks ChatGPT for a recommendation in your industry, the underlying LLM decides whether your brand is mentioned, how it is described, and whether it is positioned favourably relative to competitors.
LLM optimisation is the discipline of influencing those decisions — not through manipulation, but by ensuring that accurate, authoritative information about your brand is available across the sources that LLMs draw from.
What Are LLMs?
Large Language Models are AI systems trained on vast amounts of text data that can generate human-like responses to questions and prompts. The major LLMs powering today's AI assistants include:
- GPT-4 and GPT-4o — the models behind ChatGPT, developed by OpenAI
- Claude — developed by Anthropic, known for nuanced and detailed responses
- Gemini — developed by Google, integrated across the Google ecosystem
- Llama — Meta's open-source model family, used in various applications
- Mistral — a European AI company producing increasingly capable models
Each model has been trained on different data, uses different architectures, and may produce different recommendations for the same query. This means your brand's visibility can vary significantly across platforms — you might be consistently recommended by Claude but absent from ChatGPT, or vice versa.
Understanding these differences is essential for effective LLM optimisation.
How LLMs Decide What to Recommend
LLMs do not maintain curated directories of businesses. Their recommendations emerge from several interconnected factors:
Training data
Every LLM is trained on a large corpus of text — web pages, books, articles, forum posts, and other written content. Brands that have a strong, consistent presence across this training data are baked into the model's understanding of the world. When a user asks for recommendations, the model draws on this background knowledge.
Training data has a knowledge cutoff — information published after a certain date will not be included until the model is retrained. However, major LLM providers retrain and fine-tune their models regularly, incorporating newer data over time.
Web browsing and real-time retrieval
Many LLM-powered assistants now supplement their training data with real-time web searches. ChatGPT browses the web when answering many queries. Perplexity searches the web for every query. Google AI Overviews draw from Google's live search index. This real-time retrieval layer means that your current web presence — not just your historical presence — influences what LLMs recommend.
The mechanism that powers this is often called Retrieval-Augmented Generation (RAG). The LLM searches for relevant documents, retrieves them, and uses them to inform its generated response. Pages that rank well in traditional search are more likely to be retrieved, which means strong SEO performance supports LLM visibility.
Source authority and consensus
LLMs weigh the credibility and consistency of information across sources. A brand mentioned positively by one obscure blog is weak evidence. A brand mentioned consistently across industry publications, review platforms, expert analyses, and community discussions is strong evidence. LLMs look for convergent signals from independent, authoritative sources.
Recency and freshness
For queries where current information matters, LLMs with web browsing capabilities favour recent content. Outdated information — stale pricing, discontinued features, references to past years — signals that a source may not be reliable. Keeping your content current is a straightforward but often overlooked aspect of LLM optimisation.
Structured data and clear factual content
LLMs parse text more effectively when it is clearly structured. Content with explicit factual statements, well-defined headings, and logical organisation is easier for models to extract and reference accurately. Ambiguous or convoluted content is less likely to be used, even if it contains relevant information.
The Difference Between LLM Optimisation and Traditional SEO
LLM optimisation and SEO share common ground — both reward quality content and authoritative brand signals — but they differ in important ways:
SEO targets a ranking algorithm. LLM optimisation targets a generative model. Google's ranking algorithm evaluates pages against a set of signals and produces an ordered list. An LLM evaluates information across its entire knowledge base and produces a synthesised response. The inputs and processes are fundamentally different.
SEO is page-level. LLM optimisation is entity-level. In SEO, you optimise individual pages for specific queries. In LLM optimisation, you are shaping how the model understands your brand as an entity — across all the sources it has learned from, not just your own website.
SEO results are visible. LLM recommendations happen in private conversations. You can check your Google rankings at any time. LLM recommendations occur inside individual user conversations and vary between sessions. You need dedicated monitoring to understand your LLM visibility.
SEO generates traffic. LLM mentions generate awareness and trust. A high Google ranking sends users to your website. An LLM mention may not include a link at all — it simply names your brand as a recommendation. The value is in the endorsement itself.
Why LLM Optimisation Matters for Businesses
The shift towards LLM-powered discovery is not a future possibility — it is happening now. Several factors make LLM optimisation essential for competitive businesses:
- Consumer behaviour is changing. A growing share of product research and purchase decisions now begins with an AI assistant rather than a Google search
- LLM recommendations carry weight. Users tend to trust AI-generated recommendations because they feel personalised and authoritative
- The competitive landscape is asymmetric. Your competitors may already be investing in LLM optimisation while you are not. The brands that establish strong LLM visibility early will be harder to displace
- LLMs shape brand perception. How an AI describes your brand influences how users perceive you before they ever visit your website
Eight Practical LLM Optimisation Strategies
1. Create authoritative, factual content that LLMs can easily parse
Structure your website content with clear headings, direct factual statements, and logical organisation. Lead paragraphs with explicit claims before expanding into detail. Include specific data points, statistics, and concrete examples. This makes it straightforward for LLMs to extract and reference your information accurately.
2. Get mentioned on high-authority sites that LLMs trust
Earn coverage in industry publications, respected review platforms, and established media outlets. Each mention on a trusted source is a data point that LLMs weigh when deciding what to recommend. Prioritise quality over quantity — a single mention in a respected industry journal carries more weight than ten mentions on low-authority blogs.
3. Build presence on platforms LLMs frequently cite
Certain platforms have disproportionate influence on LLM recommendations:
- Reddit — heavily referenced by ChatGPT and other LLMs for recommendation queries
- Wikipedia and Wikidata — the most authoritative structured knowledge sources
- YouTube — increasingly referenced for reviews, tutorials, and comparisons
- Industry-specific forums and communities — carry outsized weight within their domains
- Established review platforms — Trustpilot, G2, and industry-specific review sites
An authentic, helpful presence on these platforms strengthens your LLM profile significantly.
4. Use structured data to help LLMs understand your brand
Implement comprehensive schema markup on your website: Organisation, Product, Service, FAQ, Review, and Article schemas. Structured data provides explicit context about your brand that LLMs can process more reliably than unstructured text.
5. Maintain consistent NAP across the web
Ensure your brand Name, Address, and Phone number are consistent across every platform where they appear — your website, Google Business Profile, directories, social media, and review sites. Inconsistencies confuse LLMs and reduce their confidence in recommending you.
6. Publish original research and data that LLMs reference
Original surveys, proprietary benchmarks, industry reports, and unique datasets give LLMs a reason to cite your brand specifically. When you are the primary source of a piece of data or insight, AI models must reference you when using that information. This is one of the most effective long-term LLM optimisation strategies.
7. Optimise for featured snippets
Google's featured snippets serve as a proxy signal for LLM visibility. Content that earns featured snippets is structured in the way LLMs prefer: direct answers, clear formatting, and authoritative positioning. Google AI Overviews in particular draw heavily from featured snippet content.
8. Monitor which LLMs mention your brand and track changes over time
LLM recommendations are not static. Models are retrained, browsing results change, competitors improve their presence, and user query patterns evolve. Regular monitoring allows you to identify drops in visibility before they become entrenched, spot new opportunities, and measure the impact of your optimisation efforts.
LLM Brand Monitoring
Monitoring your brand's presence across LLMs requires a different approach from traditional brand monitoring tools. Traditional tools track public mentions on social media, news sites, and review platforms. LLM brand monitoring tracks what AI models say about you in their generated responses — information that exists only in the moment it is generated and varies between conversations.
Effective LLM brand monitoring involves:
- Systematic querying — regularly asking each major LLM the questions your customers would ask, and recording the responses
- Cross-platform comparison — tracking differences in how each LLM discusses your brand, since each model has different training data and retrieval sources
- Sentiment tracking — monitoring not just whether you are mentioned, but how you are described
- Competitor benchmarking — understanding which competitors appear alongside you and how they are positioned
- Trend analysis — tracking changes in your LLM visibility over time to identify patterns and measure the impact of your efforts
This level of monitoring is impractical to do manually across five platforms and dozens of queries. RivalScope automates LLM brand monitoring across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, providing the systematic data you need to understand and improve your LLM visibility.
Getting Started
LLM optimisation is not a separate discipline from good marketing — it is an extension of it. The brands that LLMs recommend most confidently are those that are genuinely authoritative, widely referenced by trusted sources, and clearly positioned in their markets.
Start with the fundamentals: audit your current LLM visibility, identify the gaps, strengthen your content and third-party authority, and establish regular monitoring. The businesses that invest in LLM optimisation now, while the discipline is still young and many competitors are not paying attention, will build a compounding advantage as AI-powered discovery continues to grow.
RivalScope monitors your brand across 5 major LLMs — start a free 3-day trial.