Model update risk. LLMs are retrained periodically. A product that appears in recommendations today may not appear after the next training update if the underlying signals weaken or competitors strengthen their signals. Continuous signal maintenance is required.
Retrieval augmentation variability. Models with live web search (ChatGPT with browsing, Gemini, Perplexity) may pull different results on different days. Recommendation consistency is lower for these models than for base models.
Category fragmentation. If your category label is not widely used or is being redefined by competitors, your alignment work may target the wrong terms. Monitor how LLMs describe your category and adjust if the terminology shifts.
Over-optimization risk. Excessive, low-quality mention generation (spam-like content, fake reviews, irrelevant forum posts) can reduce credibility signals. LLM training pipelines increasingly include quality filters. Prioritize authentic, high-quality mentions.
| Mistake | Why It Fails | Correction |
|---|---|---|
| Optimizing for one model only | Each LLM has different training data and retrieval mechanisms. | Test and optimize across ChatGPT, Claude, and Gemini simultaneously. |
| Using marketing language in product descriptions | LLMs tend to reproduce factual statements, not promotional copy. Superlatives and subjective claims are less likely to be echoed. | Use factual, specific descriptions. Replace "industry-leading" with "used by 5,000+ companies" or a specific metric. |
| Spreading across too many categories | Dilutes category association. LLMs associate products with categories based on consistency. | Focus 70%+ of signal-building effort on one primary category. |
| Neglecting structured content | Long-form blog posts without clear, extractable statements are less useful for LLM response construction. | Add summary boxes, FAQ sections, and comparison tables to all content. |
| Treating this as a one-time project | LLM training data updates continuously. Competitors are also working on visibility. | Build ongoing processes, not one-time campaigns. Budget at least 5 hours per week for maintenance. |
| Ignoring competitor monitoring | Competitors may overtake your position without you noticing. | Track competitor mention frequency and LLM recommendation rates monthly. |
| Not tracking what LLMs say about you | The description an LLM gives of your product may be inaccurate or outdated. | Test quarterly and correct any inaccuracies by updating source content. |