What Is RAG?

Table of contents

• Definition
• Use Cases of RAG
• Conclusion

Definition

RAG stands for Retrieval-Augmented Generation. It is a method that helps AI give better and more accurate answers by finding the information it needs before generating a response.

RAG is a process that combines two things: retrieval and generation.

• Retrieval: The AI searches for information from a large database, knowledge base, or even the internet, like how we search using Google.
• Generation: After retrieving the needed information, the AI uses it to create an answer for the question you asked.

For example, if you ask an AI about a recent event or fact, instead of using only what it already knows, RAG allows the AI to find fresh, up-to-date information first. Then, it combines this new information with its general knowledge to provide a more accurate and relevant response.

This process makes AI much more useful, especially when the answers depend on the latest facts or changes that the AI might not have learned during its training.

Use Cases of RAG

RAG can be helpful in many situations, especially where up-to-date information is very important. Here are a few examples:

1. Customer Support: AI-powered systems that help with customer support can use RAG to find the latest product information, troubleshooting tips, or company updates before answering customer's questions. This ensures that the answers are always accurate. Zendesk put together an article [↗] on the subject.

2. Healthcare: In the medical field, RAG can help AI provide better advice by finding the most recent medical research, treatments, or guidelines. This can assist doctors and patients in getting the most accurate and up-to-date information.

3. Legal Services: Laws and regulations change frequently. A RAG-based AI system can retrieve the latest legal documents and rulings, ensuring that lawyers or users are provided with the most current legal advice.

In each of these cases, RAG makes sure that AI doesn’t give outdated information by checking for the latest data first.

Conclusion

In simple terms, RAG helps AI stays up to date by making sure it can look up information before giving a response.

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