Let's Discuss the Rise of Retrieval Augmented Generation (RAG)

As a deep learning technology, Generative AI is powerful and ready to transform various business domains. That said, Large Language Models (LLMs) have some "gaps" caused by outdated training data. For instance, most modern LLMs lack updated knowledge about recent industry trends or events, thus sometimes providing a problematic response that's simply out of context.

Among the recent cases, a Microsoft-powered chatbot, MyCity, provided incorrect information to New York-based entrepreneurs that could have led to them breaking city laws. In another case, Air Canada paid damages to its passengers after its AI-powered virtual assistant provided incorrect information.

By feeding LLMs with the "updated" data, enterprises can prevent GenAI hallucinations. This requirement has led to the emergence of the retrieval augmented generation (RAG) model.

What exactly is RAG, its benefits, and applications, and how can enterprises prepare for it? Let's discuss.

What is Retrieval Augmented Generation (RAG)?

Retrieval augmented generation (RAG) is essentially the process of optimizing the output of any LLM – such that it can access an authoritative knowledge source apart from its training data source. Effectively, RAG expands the capability of a traditional LLM in specific domains beyond its internal data sources.

As compared to traditional AI models that depend on pre-trained data, RAG can dynamically feed real-time external data into the AI model. With this model, AI systems can adapt to changing datasets and user queries, thus allowing them to provide contextual responses.

RAG comprises of the following 2 components:

• Retrievers – that look for real-time information from external sources or systems.
• Generative models – that produce the context-sensitive response based on the retrieved data.

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4 Benefits of the RAG model

RAG provides a host of business benefits for enterprises working with LLMs and Generative AI-powered applications:

1. Lower AI hallucination

With RAG, AI-powered systems have access to real-time factual data and do not need to work only with its training data. Traditional AI models often work with outdated training datasets, thus causing them to produce incorrect outcomes – or hallucinations.

RAG can reduce the chances of hallucinations by constantly updating its data sources. For example, an AI-enabled sales automation tool can recommend the best sales leads by accessing the latest data on prospective clients from an integrated CRM system.

2. Cost efficiency

Enterprises spend a lot of time and money in retraining their AI models. For instance, an AI-enabled chatbot (developed on a foundational model) uses API-enabled LLMs that are trained on vast volumes of unstructured and unlabeled data. For retrieving domain-specific information, enterprises incur high financial and computational costs in retraining these foundational models.

On the other hand, RAG presents a more cost-effective approach by feeding real-time data to the LLMs.

3. Improved user trust

A RAG-powered model accesses external data sources and also cites these sources to end-users. Thus, AI users now have more trust and confidence in their model responses – and can explore the sources for accuracy or validation.

For example, an AI-powered HR solution can provide an intelligent experience to employees searching for specific company policies – and even provide a link to the stored documents used in generating the response.

4. Expanded use cases

Traditional AI models are applicable for a specific purpose (for instance, to measure customer churn rate) or for limited applications. With RAG, enterprises can feed a wider range of external data to their AI models, thus enabling them to handle more user prompts or purposes.

For example, by incorporating more customer-specific datasets, the AI-powered model can provide personalized responses or insights to customer queries.

Real-world applications of RAG

RAG can help in keeping modern LLMs relevant and context-sensitive. Here are some of the real-world applications of RAG:

1. Search functionalities

With RAG, AI-powered search engines can now deliver relevant and up-to-date responses to user’s search queries (along with source citations). In this approach, the search index retrieves the relevant documents and information from its cited sources – before displaying the results to the user.

This application is useful in the eCommerce industry where search functionalities can provide the latest product recommendations in response to customer's queries.

2. Real-time content generation

RAG is a useful tool for generating relevant content. For example, an AI-powered financial market platform can retrieve the latest market updates instead of relying on static financial data. This can save hours of manual work. Additionally, this capability is effective for the online learning sector, where educational content can be personalized based on each student’s learning capacity.

3. Customer support

By using RAG, AI-powered chatbots can have a context-sensitive conversation with customers and provide effective responses to their queries. Effectively, RAG can improve chatbot capabilities with its faster data retrieval and generation model. Additionally, based on the user's feedback, chatbots can expand their knowledge base, thus elevating customer support in the long run. 

4. Medical diagnosis

In the healthcare domain, RAG can help doctors and healthcare professionals with the latest developments in medical diagnosis and treatments. This can help them arrive at a more informed decision – customized to each patient’s needs. Besides diagnosis, RAG-powered AI models are useful in:

• Designing clinical trials.
• Identifying the best treatment method.
• Discovering and developing drugs.

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How can enterprises prepare for RAG?

Here are some of the best practices for enterprises to leverage the potential of RAG:

• Define the scope of data retrieval: The first step is for enterprises to define the scope of data retrieval – or the knowledge sources they need for their business use case. This scope depends on the type of industry domain and the importance of providing relevant responses to the business.
• Focus on embedding high-quality datasets: The next step is to focus on the embedded quality of the source datasets. For example, an AI-powered healthcare tool must have embedded medical literature, which can reduce noise and provide accurate outcomes.
• Develop a multi-stage retrieval process: Instead of creating a powerful exhaustive retrieval system, enterprises must develop a multi-stage retrieval process, which can filter the best results. For example, in eCommerce, AI systems can recommend the most relevant products by filtering out the options based on the user’s needs.
• Perform iterative testing: For a successful implementation, iterative testing is essential for analyzing retrieved data and adjusting the embeddings and external sources. This is crucial for improving AI performance continuously.

Conclusion

As AI technology continues to evolve, enterprises need to leverage innovations like retrieval augmented generation (RAG) to produce more accurate and relevant responses or outcomes.

With years of expertise in AI and machine learning, Wissen can prepare you for the next wave of smarter AI solutions. Let’s connect.