• LLMs such as ChatGPT, Claude, and Gemini increasingly serve as product recommendation engines. When users ask "What's the best tool for X?", LLMs generate answers based on patterns learned during training and, in some cases, retrieved from live web data. Your product's presence in those answers depends on measurable, engineerable signals.
• There are five core signals that influence whether an LLM recommends a product by name: mention frequency, context alignment, third-party validation, structured answer-ready content, and clear category ownership.
• None of these signals guarantee inclusion in LLM outputs. LLM behavior is probabilistic, model-dependent, and subject to change with each training update. The goal is to systematically increase the probability of recommendation across models and over time.
• Most SaaS companies currently optimize for traditional search engines. LLM recommendation mechanics overlap with SEO in some areas but diverge significantly in others. This guide covers only actions that are directly relevant to LLM visibility.
• Each signal can be strengthened through specific, repeatable actions. This guide provides step-by-step implementation instructions, a 90-day roadmap, and a measurement framework.
• Compounding effects matter. Improving one signal in isolation has limited impact. Improving all five signals together increases recommendation probability more than the sum of individual improvements.
• This is an emerging discipline. What works today may shift as models evolve. Build processes that allow continuous monitoring and adaptation.

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