{
  "url": "aeoptimizer.com/faq/we-were-burned-by-a-previous-ai-llmo-vendor-how-should-we-think-about-trying-llm",
  "name": "We were burned by a previous AI/LLMO vendor—how should we think about trying LLMO strategy again?",
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    {
      "name": "We were burned by a previous AI/LLMO vendor—how should we think about trying LLMO strategy again?",
      "@type": "Question",
      "acceptedAnswer": {
        "text": "If you already had a bad experience with an “AI marketing” vendor, treat that as a signal to tighten your evaluation criteria, not to abandon LLMO altogether. The discipline itself is valuable; the execution might have been poor.\n\nOften, the failure stems from vague goals, no measurement, or treating LLMO as a one-off campaign.\n\nKey factors:\n- Whether your previous vendor defined specific prompts and metrics upfront.\n- How transparent they were about limitations and timeframes.\n- Whether they focused only on content volume instead of evidence quality.\n- How much reusable insight or infrastructure they left behind.\n\nRe-start with a small, clearly scoped LLMO audit and insist on explicit deliverables: a prompt set, visibility benchmark, and prioritized action list. Choose partners willing to challenge assumptions and show you their methodology in detail, not just their case studies.",
        "@type": "Answer",
        "description": "Use the prior bad experience to sharpen how you scope and evaluate LLMO work, not as a reason to abandon it. Start with a small, explicit audit focused on prompts and measurement, and only work with partners who can show a clear methodology and realistic expectations."
      }
    }
  ],
  "description": "Use the prior bad experience to sharpen how you scope and evaluate LLMO work, not as a reason to abandon it. Start with a small, explicit audit focused on promp"
}