In the world of AI, RAG (Retrieval-Augmented Generation) and Grounding are two sides of the same coin, but they describe different parts of the process. Understanding the distinction is key to knowing how AI stays factually accurate.
RAG: The Technical Engine
RAG is the technical method. It is the “plumbing” that connects a Large Language Model to external data. When you ask a question, the RAG process follows three steps:
- Retrieve: Searching for relevant documents or data.
- Augment: Adding that data into the AI’s instructions.
- Generate: Writing a response based on that added info.
Grounding: The Anchor to Reality
Grounding is the intended outcome. It is the act of tethering the AI’s response to verifiable facts so it doesn’t “hallucinate.” While RAG is the journey, Grounding is the destination—the certainty that every word spoken is rooted in an actual source.
Is Grounding the act of searching, finding, and citing?
Yes. While RAG describes the “how,” Grounding describes the “what.” In practice, you can define Grounding as the process of:
- Searching: Actively seeking out the most current info.
- Finding the Source: Identifying the “Grounding Chunks” (the specific evidence).
- Citing: Creating a digital trail that proves exactly where a fact came from.
Summary of Differences
| Feature | RAG | Grounding |
| What it is | The technical workflow. | The factual constraint. |
| Focus | Efficiency of data movement. | Accuracy and proof of claims. |
| End Result | A response with extra context. | A response with verifiable citations. |
In short: RAG gives the AI the book to read; Grounding makes sure the AI actually quotes the book instead of making up its own story.