Episode 37: What Is RAG? The AI Technique That “Looks Things Up” Before Answering

Can We Really Trust AI’s Answers?

As AI becomes more familiar in our daily lives, you may have found yourself wondering, “Is this answer really correct?” For example, maybe you asked a chatbot a slightly technical question and got a response that sounded convincing—but was actually a bit off. If that sounds familiar, you’re not alone.

These kinds of mismatches often happen because the AI is answering based only on what it already knows. In other words, it’s relying solely on the information it learned beforehand. That means it can struggle with newer topics or subtle nuances.

To address this issue, a new approach called “RAG” (Retrieval-Augmented Generation) has been developed.

What Is RAG? A System That Lets AI “Look Things Up” Before Answering

RAG is sometimes translated into Japanese as “search-augmented generation.” While the term might sound technical at first, the idea behind it is quite simple. Imagine an AI that takes time to do some research before giving you an answer—that’s essentially what RAG does.

More specifically, it works by having a large language model (LLM) search for relevant information from online articles or databases before crafting its response. Instead of just speaking from memory, the AI checks reference materials to help explain things—much like how we humans might look something up before discussing it.

It’s similar to when you’re unsure about a topic and decide to consult a book or website before sharing your thoughts. That’s exactly the kind of behavior RAG brings to AI.

Where RAG Shines—and What Challenges It Faces

Let’s say you ask an AI about a recently revised law. A typical AI might give you outdated information because its knowledge stops at the point when it was last trained.

But with RAG, the system can search for recent articles or official documents related to your question and read through them before answering. It’s like having a friend who visits the library and carefully researches your question before explaining it to you.

Thanks to this approach, AI is evolving into a more reliable and trustworthy source of information.

That said, RAG isn’t without its challenges. First of all, not everything found through search is guaranteed to be accurate. Also, gathering bits of information from different sources and turning them into smooth, human-like sentences requires careful coordination.

Even so, this method represents an important step forward in overcoming one of AI’s biggest limitations: its fixed knowledge base. Looking ahead, technologies like RAG are expected to play key roles in areas where deep expertise is essential—such as healthcare, legal services, and education.

In addition, supporting technologies like “embedding” and “vector search” are crucial parts of making RAG work effectively (we’ll explore these in detail in another article).

RAG: A Clue to Understanding “How Did You Come Up With That Answer?”

You might not be familiar with the term “RAG” yet. But once you understand it as “a system where AI looks things up before answering,” its value becomes much easier to grasp—and perhaps even relatable.

After all, we humans also tend to check facts before talking about something we’re unsure of. In that sense, there’s something quite approachable about this technology.

As our interactions with AI deepen over time, asking questions like “How did this answer come about?” will become increasingly important.

And RAG offers one thoughtful way for AI to respond honestly to that kind of question.