Most explainer content about AEO makes the same mistake. It describes the mechanics in the abstract — citation, prompts, share of voice, recommendation surfaces — without ever showing what the actual conversation between a buyer and an AI tool looks like. That is the wrong way around. The conversation is the thing. Understanding what buyers literally type, and how the AI literally responds, is what separates a real AEO strategy from a checklist of buzzwords.
Three real prompts — and what each one triggers
Consider three prompts a legal tech buyer might plausibly enter. Each triggers a different kind of synthesis, draws on a different set of source domains, and produces a fundamentally different shape of answer:
| The prompt | What kind it is | What the AI does with it |
|---|---|---|
| “What are the best discovery platforms for a midsize litigation firm?” | Broad / category | Pulls a wide source set and returns a ranked shortlist; the brand named most consistently tends to win. |
| “What are the main alternatives to Relativity for firms that find it too expensive?” | Comparative | Anchors on one incumbent and surfaces challengers; positioning against that incumbent decides inclusion. |
| “Is Everlaw worth the price for a firm handling fewer than 100 matters a year?” | Specific / skeptical | Weighs sentiment and reviews; candid third-party validation dominates the answer. |
The AEO discipline is, at its core, the work of making sure your brand appears appropriately across all three answer types — not just the flattering one.
How citation actually works
When an AI tool synthesizes an answer, it is not running a single search and returning the top result. It pulls from a constellation of sources — sometimes a dozen or more — weighted by what its underlying model has learned to trust for that category. For legal tech, the sources that consistently shape answers include:
- The major legal trade publications
- The relevant G2 and Capterra category pages
- Vendor websites (weighted less heavily than vendors would like)
- Reddit threads in the legal subreddits
- YouTube videos walking through product demos
- Analyst blogs from voices like Bob Ambrogi and Dennis Kennedy
The brand that appears most consistently and most positively across this constellation is the brand that gets recommended.
Why ChatGPT and Perplexity sometimes disagree
The three major tools do not produce identical answers to identical prompts.
ChatGPT tends to be more synthetic and less citation-heavy.
Perplexity is more aggressively citation-anchored and updates faster with recent web content.
Gemini sits between them and over-weights Google-indexed sources for reasons that should surprise nobody.
A complete measurement framework tracks all three, because the same brand can have very different visibility profiles across them — and a strategy that optimizes for one tool misses two-thirds of the picture.
The diagnostic worth running today
Try this fifteen-minute exercise before you read another framework:
- Open ChatGPT and enter a prompt a real buyer in your category might use — specific enough to be plausible, broad enough to elicit a vendor recommendation.
- Read the answer carefully: note which competitors appear, which sources are cited, and whether your brand appears at all and in what tone.
- Repeat on Perplexity and Gemini, then compare the three.
If your brand is missing from all three answers, the visibility gap is real and currently costing you pipeline. If it appears but is described inaccurately or unfavorably, the sentiment gap is the more urgent problem. If it appears favorably across all three, the question becomes whether that holds for the dozens of other prompts your category supports.
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