LLMs read your comment section

Earlier this year I had a conversation with people from a large software company that stuck with me. Their observation: when you ask an AI assistant about a brand, the answer leans surprisingly heavily on public sentiment. Forum threads, review sites, the comments under social posts. Not the carefully written product pages. Not the press releases.

Think about what that means for a moment. For years, a critical comment under an Instagram post was a small customer service issue. Someone is annoyed, maybe you reply, maybe you don't, and a week later nobody remembers it.

Now that comment is training material for how machines describe you.

The public picture is skewed

Here's the uncomfortable part. Most brands with a loyal community have a lopsided public footprint:

• The happy customers talk in closed places. Partner portals, private groups, internal forums, direct conversations with their contact person. Lots of goodwill, almost none of it visible to a crawler.
• The unhappy customers talk in public. A review site, a comment under your latest video, a thread in an open forum. That's where people go when they feel they aren't being heard anywhere else.

So the public picture is often worse than reality. And the public picture is the one that AI assistants see.

You can't fix that with more ads or a better About page. You fix it where it happens: in the comments.

Social listening becomes narrative work

Social listening used to be a reporting job. Count mentions, measure sentiment, put a chart in the monthly deck. Useful, but passive.

In the AI age, it becomes narrative work. The question isn't just "how do people feel about us?" but "what story does the public record tell about us, and are we part of that story?"

That changes what a good reply looks like.

The canned reply makes it worse. "We're sorry to hear that, please contact our support team." Everyone has seen this reply a hundred times. It tells the reader, and any machine reading along, that the problem is still unsolved and the brand didn't engage with it.

The specific reply changes the record. Answer the actual problem, in public, in a few sentences. If it's a known issue, say what the fix is. If it needs a conversation, say who will call and when. The next person with the same problem finds the answer right there, and so does the AI.

Then close the loop. When someone's problem has really been solved, it's fair to ask whether they'd update their review or add a comment. Many will, because they were never angry at the brand, they were angry at being ignored.

Where AI agents help

This is a lot of work if you do it by hand across several accounts and platforms. It's exactly the kind of work where agents shine, as long as a human stays in charge.

Here's the setup I've been building up over the last few months:

1. A weekly sentiment run. An agent pulls comments and messages from all brand accounts, scores the sentiment of each one and groups them by topic.
2. A "still waiting" list. Everyone who asked a question or complained and hasn't had a real answer yet. Not a chart. A list of people. One lesson here: most "answered" messages in our analytics turned out to be automatic replies within seconds. We now treat those as unanswered.
3. Drafted replies. For each open item, the agent drafts a reply that follows our own guidelines: tone of voice, what we say about known issues, when to hand over to support. The drafts are starting points, not autopilot.
4. A human checks and posts. Always. The agent doesn't have the context to know whether this customer already talked to someone yesterday, and it shouldn't speak for the brand on its own.
5. Actively collect good reviews. A few new reviews every week from customers who are clearly happy, so the public picture isn't defined only by the loudest few.

None of this is sophisticated technology. It's a routine, and the agent makes the routine cheap enough to actually keep up.

Reconstructed view with made-up data: the real tool looks like this, but every name and number here is invented.

What not to do

A few things I'd avoid:

• Don't let the agent post on its own. One badly judged automatic reply to an angry customer, in public, undoes a lot of careful work.
• Don't argue in public. If a review is unfair, state the facts once, calmly, and offer to talk. The reader decides who looks reasonable.
• Don't fake it. Paid or invented reviews are not a shortcut. They're a liability, for people and machines alike.
• Don't confuse volume with coverage. Ten fast replies to easy questions don't make up for one unanswered complaint that sits there for a month.

The short version

AI assistants learn about your brand from what's public. What's public is often skewed towards the unhappy few, because the happy many talk elsewhere.

So treat every public comment as part of your brand's story. Answer the real problem, in public, like a human. Use agents to find everything that's waiting and to draft the replies. Keep a person on the send button.

Every comment is a customer. And, these days, every comment is also a source.