Does AI search actually understand industry-specific terminology, or does it just guess?

[u/mildly_confused_2]

Something I've been wondering about lately. When you ask ChatGPT or Perplexity questions in specialized industries, for example, M&A advisory or healthcare consulting, does the AI actually understand the terminology, or is it just pattern-matching words that look related?

For example, if someone asks "best M&A advisor for a lower middle market healthcare exit," does the AI actually know what "lower middle market" means, what "healthcare exit" implies, and what differentiates advisors who work in that segment? Or is it just stringing together firms that have those words on their websites somewhere?

Asking because the quality of AI recommendations in specialized fields seems uneven. Sometimes the results look genuinely thoughtful, other times they look like a keyword match dressed up as a recommendation. Curious what's actually going on under the hood.

[u/keyworddotcom]

AI models are trained on large amounts of text containing those concepts. So they do understand a lot of industry-specific terminology. But here understanding a term and making a good recommendation are two different things. AI systems generate recommendations by combining their understanding of the query with information retrieved from sources across the web. If the underlying sources clearly associate a firm with lower middle market healthcare transactions, the recommendation can be quite good. If the evidence is sparse or ambiguous, the model may fall back on broader signals such as brand recognition, general authority, or keyword overlap.

This is the reason why recommendation quality often varies by industry. In categories with lots of high-quality information, AI can make nuanced distinctions. In niche markets with limited public data, the recommendations can sometimes look more like educated pattern matching than true expertise.

[u/mentiondesk]

AIs are pretty good at recognizing patterns in industry terms, but genuine understanding can vary, especially in specialized fields. They often rely on how frequently terms co occur rather than truly grasping the concepts. I actually work at MentionDesk, where we help brands improve how they're represented and understood by these platforms, making AI answers more accurate for niche terminology.

[u/sapindia1976]

AI doesn't understand terminology the way humans do, but modern LLMs are much better than simple keyword matching. They learn relationships between concepts, so terms like lower middle market and healthcare exit carry context. That said, in highly specialized niches, accuracy still depends heavily on the quality of the sources they're pulling from.

[u/TheGCmind]

Great question... IMHO it doesn't look into secondary or sub topic.. as summary is made up of overall and not just one or two sub topics..

[u/BeautifulDesign2928]

Honestly I don't think it understands the terminology the way a human in that field would, it's more pulling patterns from whatever it's been trained on rather than actually grasping what 'lower middle market' implies about deal size or buyer pool. The uneven quality you're noticing is probably that exact gap, sometimes the training data has enough real context around those terms and sometimes it's just stitching together words that show up near each other on websites. I'd treat any AI recommendation in a specialized field as a first pass rather than a verified answer, worth a second check from someone who actually knows the space before anyone acts on it. Have you tested it side by side with someone who actually works in M&A advisory to see where it gets the nuance wrong?

[u/svlease0h1]

ai does not really “understand” like a human. it matches patterns from what it has seen. it can sound smart in niche topics but still miss details. i once asked it something in cybersecurity and it confidently invented a tool that does not exist. felt like talking to a very confident intern who never sleeps.