Improve AI Accuracy and Reliability with Retrieval-Augmented Generation (RAG)

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Artificial Intelligence (AI) has been transforming the technology landscape, enabling the creation of advanced solutions for automation, data analysis, and virtual assistants. However, a recurring challenge in Large Language Models (LLMs) is the accuracy and reliability of the generated responses. Often, these models make mistakes when generating information or rely on outdated or unreliable sources.

The Reliability Problem in AI Models

AI models depend on the data they have been trained on. However, this data may be outdated or lack specific information required to answer certain questions. Additionally, AI can generate incorrect or imprecise responses as it lacks an intrinsic mechanism to verify the truthfulness of the information.

To address this issue, we can use a technique called Retrieval-Augmented Generation (RAG).

What is RAG and Why is it Useful?

RAG (“Retrieval-Augmented Generation”) is a technique that enhances the accuracy and reliability of AI-generated responses. It combines two essential components:

1. Retriever: Responsible for fetching relevant information from a predefined knowledge base, retrieving data that complements or updates the model’s knowledge.
2. Generator: Creates responses based on the retrieved data, ensuring that the AI provides up-to-date and trustworthy content.

This approach ensures that generated responses are always aligned with a reliable and verifiable context. Additionally, RAG prevents the model from relying solely on pre-trained data, which may be outdated or incomplete.

Advantages of Using RAG

• Cost Reduction ?: Eliminates the need for retraining massive models, as data can be updated independently of the AI.
• Higher Accuracy ??: Responses are based on validated data, minimizing errors.
• Transparency ??: The sources used in the response can be displayed, allowing users to verify the information.

How Does RAG Implementation Work?

The implementation of RAG can be divided into three main stages:

1. Building the Knowledge Base

First, it is necessary to create a repository of information that the AI will use as a source. This involves:

• Extracting text from documents.
• Converting text into vectors using embedding models.
• Storing the vectors in a vector database.

2. Data Retrieval

When a user asks a question, the AI:

• Converts the query into a vector.
• Searches for relevant documents in the vector database.
• Selects the most relevant documents to generate the response.

3. Response Generation

The AI model receives the user query along with the retrieved documents and generates a response based on the provided data.

Practical Example of RAG

Let’s imagine a customer support system for a real estate company. By using RAG, a chatbot can answer queries based solely on the company’s internal documents. If a customer asks about the refund policy, the system will:

1. Search for the most relevant documents related to refund policies.
2. Generate a response based on these documents.
3. Display both the response and the source document for verification.

If the user asks something that is not in the knowledge base, the system will respond: “I don’t know”, ensuring that incorrect information is not provided.

Conclusion

Retrieval-Augmented Generation (RAG) is a powerful solution to ensure that AI models provide more accurate and reliable responses. By using up-to-date knowledge bases and efficient information retrieval, it is possible to overcome common challenges in generative AI and provide greater transparency and security in responses.

If you want to see this in action and learn more about how to implement this approach with this incredible video that Yohan Lasorsa recorded about it:

Have you used the RAG approach in your AI projects? Share your experience in the comments! ??

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