How RAG keeps Expertise AI responses in control

One of the most common questions we hear from B2B companies is this:

“If a customer asks my AI agent about a competitor… what will it say? Will it promote them? Will it make comparisons we never approved?”

It’s a perfectly valid concern. Yes, your AI agent should be natural, smart, and helpful. But it also needs to stay controlled, accurate, and aligned with your brand voice.

With Expertise AI, you never have to worry about it going off-script. And the reason behind that control is a powerful AI framework: Retrieval-Augmented Generation (RAG).

In this blog, we’ll explain what RAG really is, how it makes Expertise AI more reliable, and what you can do to keep your AI agent fully in control.

Generic AI chatbots like ChatGPT, Perplexity, and Gemini don’t understand your brand story. They don’t know how you position your product, what makes you different, or what unique benefits your product delivers.

So what happens when someone asks them a question about your brand, or your competitors’ brands? Well, these tools typically pull information from their massive training datasets or scan the internet in real time. They prefer sources like review platforms, authoritative websites, comparison blogs, social media posts, or industry forums.

That means these AI chatbots might:

• Compare you to a competitor you’ve never mentioned.
• Promote or praise a competitor based on public opinions.
• Use a tone that feels completely off-brand.

So you have very little control over how they respond.

Even though Expertise AI helps influence how generic LLMs talk about your brand through programmatic SEO pages, you still can’t fully control what these chatbots decide to say.

And that’s the primary difference. Unlike generic AI chatbots, the Expertise AI agent gives you complete control over every response it generates. It always stays accurate, consistent, and safe. Let’s look at how it works.

Retrieval-Augmented Generation (RAG) is a framework that allows AI to extract information from an external data source before generating a response. It ensures the answer is accurate, relevant, and based on verified content.

So, RAG is your safety layer for controlled, on-brand AI. It ensures your Expertise AI agent only talks about what exists in your company’s approved training data.

It works in three simple steps:

• Retrieval: When a visitor asks a question, the AI searches and retrieves relevant information from your training data: this could be your website content, documents, guides, or FAQs.
• Augmentation: The AI combines the retrieved information with the visitor’s original query to create a more contextual prompt.
• Generation: Expertise AI then uses its LLMs (such as GPT-5.1, Claude Sonnet 4.5, and others) to generate a natural, helpful response.

So your Expertise AI agent responds using only the content you’ve uploaded, like website pages, support articles, and product documentation. It never browses the internet or pulls information from external sources. 

This is what makes Expertise AI reliable. The AI simply cannot wander outside your knowledge base.

Since the response of the AI agent completely depends on the materials you provide, let’s understand this through two simple cases:

Case 1: Your material mentions competitors.

For example, you may have a comparison page on your website, a blog like “You vs. Competitor X”, or an article on top alternatives in your category.

If your content includes information about competitors, then your AI will follow that exact tone and context. Nothing more. It doesn’t invent new comparisons. It doesn’t change your message. It simply reflects your positioning exactly the way you wrote it.

Case 2: Your material doesn’t mention competitors at all.

In this situation, the AI won’t bring them up. It won’t search the web. It won’t guess. It won’t make comparisons. Instead, it will keep the conversation focused on your own strengths and offerings, just like a trained sales rep.

Example:

User: “How do you compare to Competitor X?” Your AI: “I don’t have information about Competitor X, but here’s what we offer…”

Clear. Safe. On-brand.

Here are a few ways to ensure your Expertise AI agent always responds correctly:

1. Keep your training materials up to date.

Make sure your AI is trained on your latest content. Simply log in to Expertise AI, open the Training Materials tab, and add new URLs or documents. If you update a web page, then retrain the AI so responses stay aligned.

2. Test your AI regularly.

Go to Try My AI and ask questions related to your product and competitors. If you don’t like a response, open the Conversations tab and improve how the AI agent responds to similar queries in the future.

3. Identify and fix knowledge gaps.

The Knowledge Gaps feature shows you queries that the AI struggled with due to limited training content. You’ll find this data on the Dashboard, and it helps you upload relevant material to improve future responses.

4. Track visitor conversations.

Check the Conversations tab to see how users interact with your AI in real time, especially competitor-related questions. If any response feels off, you can instantly rewrite and improve it.

Now you know exactly how your Expertise AI agent handles competitor-related questions. Because it’s powered by RAG, it only uses your approved training materials to generate responses, never internet sources. But you also need to keep your training material updated and complete to ensure the best performance.

We’re constantly improving our demand conversion platform to help you turn more website traffic into a qualified pipeline. We add new features, latest AI models, and deeper integrations every month. Connect with us on LinkedIn to never miss an update.