RAG, retrieval-augmented generation: a revolution for generative AI and chatbots

Retrieval-Augmented Generation (RAG) is an AI model developed to enhance text generation by leveraging external data. Unlike purely generative models, which rely solely on the information in their training datasets, RAG integrates an information retrieval component. This dual approach allows the model to search for and utilize external documents in real-time to enrich generated responses.

Example of RAG for businesses: 

Imagine an online retail company that wants to provide exceptional customer service. They want customers to use a chatbot to ask questions about products, orders, returns, company policies, and receive fast and accurate responses.

The Problem:

A general language model (LLM) might answer general questions about products but wouldn't provide specific, up-to-date information like the current status of an order, details of an ongoing return, or special promotions.

The Solution:

RAG allows the chatbot to use its vast information resources, such as product databases, order histories, and internal news feeds, to provide more precise and current responses.

The company has numerous information sources: product databases, order histories, internal blogs, news feeds, customer chat transcripts, etc. RAG converts all these data into a common format and stores them in a library accessible by AI. These data are then transformed into digital representations through integrated language models and stored in a vector database for quick searching and utilization.

RAG operates in two main steps:

1. Information retrieval

   
   
   • When a question or query is posed, the RAG model begins by searching for relevant documents in an external database, which can contain millions of documents, scientific articles, web pages, or books
   • An advanced search model identifies the most relevant documents to the query

2. Text generation

   
   
   • Once the relevant documents are retrieved, the model uses this information to generate a response. The text generator, often a Transformer model like GPT-3, incorporates the retrieved data to produce a response that is both coherent and enriched with up-to-date, specific information
   • This approach allows the model to generate responses that are not limited by the static knowledge from its training period but can include recent and relevant information

When someone asks a question to a chatbot, they expect an instant response. Therefore, speed and user-friendliness are crucial. However, most chatbots are trained to respond to a limited number of specific requests, known as intents. RAG can enhance these chatbots by providing natural language responses to questions not covered in the predefined list of intents. This technology is particularly suitable for chatbots as users expect precise and contextually relevant responses, often based on specific contexts.

For instance, a customer asking about a new product needs specific data about that product, not about a previous version.

RAG can significantly improve the efficiency and accuracy of chatbots by enabling them to generate contextual and precise responses, better meeting user expectations. Learn more about how to build customer loyalty with generative AI chatbots.

Advantages:

• Knowledge update: RAG can access recent information, overcoming one of the main limitations of traditional generation models
• Accuracy and relevance: by using specific and relevant documents, the generated responses are often more accurate and contextually appropriate
• Flexibility: the model can be applied to various databases and domains, making its use very versatile

Challenges:

• Technical complexity: implementing RAG requires robust infrastructure to handle real-time retrieval and generation
• Data quality: the relevance of responses heavily depends on the quality and relevance of the retrieved documents

DialOnce enables the creation of chatbots using RAG technology. With this technology, DialOnce provides more precise and contextual natural language responses, improving user interactions while ensuring confidentiality and information protection. The customized models developed are exclusive to each client, ensuring maximum security. Book your demo now!