What is RAG (Retrieval-Augmented Generation)?

What Is RAG?

RAG (Retrieval-Augmented Generation) is the technique AI models use when they don't have enough information in their base knowledge to generate a complete response. Think of RAG like an open-book exam.

The Three Components of RAG

RAG combines three key capabilities that work together seamlessly:

1. Retrieval: searching for relevant information
2. Augmentation: supplementing base knowledge with the information found in retrieval
3. Generation: creating responses

How RAG Works in Practice

If you've ever used an AI model like ChatGPT, you're using the model's consumer app, which uses both base model knowledge learned from its pre-training phase and RAG when needed to find information it doesn't inherently know.

Here's what happens behind the scenes when you prompt an AI model:

Step 1: User Prompts an AI Model

You ask a question or make a request.

Step 2: The Model Evaluates Its Base Knowledge

The model determines if it knows enough from its base knowledge to generate a response. It evaluates whether it has sufficient information from its training data alone.

Step 3: Retrieval Kicks In (If Needed)

If it can't find all the information it needs, the AI runs multiple queries in a search index 

Step 4: The AI Augments Its Knowledge

The AI evaluates results and visits many URLs (often many more than a human would) gathering more information to supplement its existing knowledge.

Step 5: Response Generation

The AI generates a response based on both its training AND the retrieved information, citing selected sources it visited.

The Hidden Problem Most GEO Platforms Miss

Most GEO platforms only have access to the consumer apps, which show a blended view of base model knowledge and RAG sources, without any way to separate the two.

This matters because 62% of ChatGPT responses come solely from its base model knowledge (Evertune Research). That means more than half of what AI models "know" about your brand isn't coming from your website or recent content. It's coming from what the model learned during its pre-training phase.

The Questions You Can't Answer Without Base Model Isolation

If you can't isolate base model knowledge from RAG-augmented responses, you can't answer fundamental questions about your AI visibility:

• Is my brand weak in the base model but strong in search results?
• Are the sources being retrieved actually relevant to my category?
• What happens when AI agents rely purely on API access without search integration?

The Evertune Difference

Evertune's approach is different. In addition to accessing the consumer apps, we also have direct API access to the AI models to isolate base model responses, allowing you to see the foundation of how AI models perceive your brand.

Why Isolating the Base Model Is Important

When you can compare base model responses (via API) to consumer app responses (base model + RAG), you unlock two critical capabilities:

1. Identify Your True Performance Drivers

You can identify situations where your brand has massive outperformance in the consumer app. This allows you to differentiate between sources that are irrelevant to your brand, category, or preference and those that are highly relevant and therefore likely provided the context that made the model give a materially different answer in the consumer app than it would have in the base model.

Example scenario:

Imagine your brand shows up in 15% of base model responses but 65% of consumer app responses. That gap tells you that specific RAG sources are driving your visibility—and you can identify exactly which ones matter most. Without base model isolation, you'd just see the 65% number and assume you're doing well across the board.

2. Future-Proof Your Brand for AI Agents

AI agents are built through API integration, which means they access the base model directly without the search layer that consumer apps provide. Understanding the responses direct from the API makes you ready for agents. You're future-proofing your brand.

When agentic AI becomes mainstream—and it's happening faster than most marketers realize—those agents will be making autonomous decisions based primarily on base model knowledge. If your brand isn't embedded in that foundation, you won't even make it to the consideration set when agents are researching solutions on behalf of users.

The Bottom Line

The brands that establish strong presence in both the base model and high-impact RAG sources will dominate AI-generated recommendations. The ones that only optimize for one layer—or worse, can't even distinguish between the two—will struggle to maintain visibility as AI search becomes the primary way customers discover and evaluate solutions.

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