LLM Optimisation: How to Make Your Brand Visible in AI-Generated Answers

Most SEO teams are still obsessing over Google rankings while a quiet shift is underway. More users are getting answers directly from ChatGPT, Perplexity, Claude, and Gemini without ever clicking a search result. If your brand is not showing up in those AI-generated answers, you are missing a growing channel, and traditional SEO will not fix it.

LLM optimisation, sometimes called GEO (Generative Engine Optimisation), is the practice of improving how large language models understand, represent, and cite your brand, product, or content. It is distinct from SEO, though the two overlap. Here is what you actually need to know to get started.

What "LLM optimisation" means in practice

Search engine optimisation targets crawlers and ranking algorithms. LLM optimisation targets training data, retrieval-augmented generation (RAG) pipelines, and the reasoning patterns models use when deciding what to cite.

When a user asks Perplexity "what is the best tool for X," the model does not run a live crawl and rank pages. It draws on what it learned during training, combined with real-time retrieval in some cases. Your job is to ensure that when any model encounters your category, your brand comes up as a credible, well-understood answer.

This means optimising for three things: clarity (the model understands exactly what you do), authority (your brand appears in trusted sources the model has seen), and citability (your content is structured in a way that makes it easy for a model to extract and surface a clean answer).

Why traditional SEO tactics fall short

High domain authority helps, but it is not sufficient. A site can rank well on Google yet be almost invisible to AI answer engines. This happens because LLMs weight different signals than Google's algorithm does.

Models favour content that is unambiguous, well-structured, and corroborated across multiple independent sources. A page that relies on implicit brand recognition, vague category language, or thin content may rank fine on Google but leave LLMs unable to form a clear picture of what the brand actually does.

Keyword stuffing is actively counterproductive for LLM optimisation. Models are trained to synthesise meaning, not to count keyword density. What matters is whether your content makes clear, direct, verifiable statements about your product's capabilities, use cases, and differentiators.

Practical steps to improve your LLM visibility

Be explicit about what you do. Every key page on your site should state plainly, in prose, what your product does, who it is for, and what problem it solves. Do not assume the model infers this from context. If your homepage says "the future of intelligent workflows," rewrite it to say what the product actually does.

Build structured, citable content. FAQs, comparison pages, and definition-style content perform well in RAG pipelines. When a model is asked a question, it often retrieves short, self-contained passages. Pages with clear headings, concise answers under those headings, and minimal promotional noise are easier for models to cite cleanly.

Get mentioned in independent sources. Training data skews toward content that appears across multiple authoritative, independent sources. This means you need mentions in editorial publications, niche community forums, GitHub discussions, product review sites like G2 and Product Hunt, and industry newsletters, not just your own site.

Maintain an llms.txt file. Following the emerging llms.txt convention, placing a structured summary of your product at /llms.txt on your domain gives AI crawlers and RAG systems a clean, authoritative description of your brand. It is a small effort with a potentially meaningful payoff as more AI systems adopt it.

Keep your category language consistent. If you describe your product differently on your homepage, your docs, your G2 profile, and your press releases, models receive conflicting signals. Pick precise category terms and use them consistently across all public-facing surfaces.

How community mentions factor into LLM training data

A significant but underappreciated signal is community discussion. Reddit threads, Hacker News comments, Stack Overflow answers, and G2 reviews are well-represented in LLM training corpora. When real users discuss your product in these spaces, models learn to associate your brand with specific use cases, problems, and sentiments.

This has two implications. First, genuine community presence matters. If people are talking about your product organically in relevant communities, that creates positive signal. Second, negative or confused community sentiment can create negative signal. A thread of users complaining that your product is hard to use, or worse, threads where your category is discussed without your brand appearing at all, tells models something unflattering.

Monitoring these conversations is therefore not just a customer success function. It is an LLM optimisation function. Knowing where your brand is mentioned, how it is characterised, and where competitors are being recommended instead gives you the raw material to close gaps.

bing.ly tracks exactly this. It monitors Reddit, Hacker News, and G2 alongside direct AI visibility in ChatGPT, Perplexity, Claude, and Gemini, so you can see both where your brand appears in AI answers and how community discussion is shaping your AI reputation.

Measuring LLM visibility

You cannot optimise what you cannot measure. The challenge with LLM visibility is that there is no equivalent of Google Search Console giving you impressions and clicks from AI systems. You have to query the models directly, systematically, and track the results over time.

Useful questions to run across models include category queries ("what are the best tools for X"), problem-framing queries ("how do I solve Y"), and direct brand queries ("what does [your brand] do"). Track whether you appear, at what prominence, which competitors appear alongside or instead of you, and how the models characterise your product.

Do this regularly, not once. Model outputs shift as training data updates, as new content enters the retrieval pool, and as the models themselves are updated. A brand that appears prominently today may drop off after a model update if newer contradictory signals have entered the training pipeline.

The intersection with competitor tracking

LLM optimisation is also a competitive intelligence exercise. When you query an AI system about your category and your competitor appears instead of you, that is actionable information. You need to understand what they are doing differently: are they getting more editorial coverage, more community mentions, or producing more citable structured content?

Running the same queries across multiple models and comparing results gives you a map of where you are strong, where you are weak, and where specific competitors are outperforming you in the AI layer. This is the work that separates teams doing serious GEO from those who are simply hoping their existing SEO will carry over.

Getting started without overcomplicating it

LLM optimisation is still early. The practices are not fully codified, the measurement tooling is nascent, and no one has perfect answers. That is actually an advantage if you start now. The brands that build a clear, consistent, well-cited presence across the web and in community discussions today will compound that advantage as AI answer engines become more central to how people discover products.

Start with the basics: clear on-page language, structured citable content, an llms.txt file, and a deliberate effort to earn mentions in independent sources and communities. Then measure your AI visibility systematically so you know whether the work is moving the needle.

bing.ly was built for exactly this workflow, combining AI answer monitoring with community intelligence in one place, priced for small teams and founders. If you want to see where your brand stands in AI-generated answers right now, and which community discussions are shaping that picture, it is worth a look.