How Retrieval Augmented Generation (RAG) Enhances Gen AI

RAG is an innovative technique that enhances the capabilities of LLMs by integrating them with context-specific data
RAG is a technique that promises to improve the way Gen AI fetches answers and provide business with a more reliable use case for client-facing uses

As generative AI (GenAI) systems are becoming increasingly prevalent, enterprises across industries are looking to implement them in all manner of ways to optimise their operations. 

Customer service chatbots, intelligent assistants, and domain-specific research tools - the implementations are vast.

However, as has been witnessed with some GenAI models, accuracy can remain an issue. 

Therefore, there is a growing need to improve the accuracy, relevance, and reliability of these systems so businesses can confidently implement them in real-world, mission-critical applications.

One promising approach to this problem that has emerged is RAG. RAG is an innovative technique that enhances the capabilities of LLMs by integrating them with context-specific data. This significantly improves the accuracy, relevance, and reliability of AI-generated responses.

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What is RAG and how does it work?

Siddharth Rajagopal, Chief Architect, EMEA at Informatica, explains: "In essence, RAG combines static LLMs with context-specific data. And it can be thought of as a highly knowledgeable aide. One that matches query context with specific data from a comprehensive knowledge base. For example, if a customer wants to understand the latest trends and pricing for a specific fashion item, RAG can access the most recent fashion magazines, retail websites or online reviews to enhance the LLM and offer up-to-date information on product trends, availability and pricing."

Shane McAllister, Lead Developer Advocate (Global) at MongoDB, further simplifies the concept: "In simple terms, Retrieval Augmented Generation (RAG) is a process to improve the accuracy, currency and context of large Language Models (LLMs) like GPT-4. LLMs are already trained on vast swathes of public data, often much of the internet, to generate their output. RAG can extend these capabilities to private data and specific domains - be it an industry or a specific organisation."

RAG systems can provide more accurate and current information by pulling in relevant data from external sources, tailoring LLM responses to specific needs by incorporating domain-specific data.

RAG is particularly well-suited for applications that require precise and informed answers. Adam Lieberman, Chief AI Officer at Finastra, notes: "RAG is a great choice for search and discovery query-based applications where precise and informed answers are crucial. For example, customer support use cases in financial applications, medical or legal research, or education and tutoring."

However, these capabilities of RAG are enabled by the strength of other technologies.

Maxime Vermeir, Senior Director of AI Strategy at ABBYY, states: "RAG is enabled by other technologies, such as NLP and Purpose-Built AI that allow it to have access to highly structured and consistent knowledge base. Nonetheless, RAG's role will be crucial in making GenAI more reliable and contextually aware."

Addressing AI hallucinations

A significant challenge with LLMs is their tendency to hallucinate, or generate incorrect information. As McAllister notes: "We've all seen recent stories in the news about LLMs hallucinating, and the very real, negative impacts AI hallucinations have had on the owners of those LLMs.” 

In addition to its ability to delivery highly accurate and current information, RAG offers solutions to the issues LLMs are facing.

Joe Mullen, Director of Data Science & Professional Services at SciBite, explains: "LLMs are likely to hallucinate because they regularly rely on outdated information that can be difficult to trace, and can have security and privacy-related vulnerabilities. These issues can be combated by grounding AI."

David Colwell, VP of AI and ML at Tricentis, adds:  "RAG addresses this problem by combining the power of LLMs with external knowledge sources, such as a variety of text-based documents and other types of data sources, to generate more accurate and informative responses."

By using up-to-date and relevant data, RAG reduces the likelihood of hallucinations and enhances the reliability of AI responses.

Misconceptions and limitations

Despite its advantages, RAG faces challenges. First is in the misconception of what it is, which can impede implementation or wider rollout.  

Adam Lieberman, Chief AI Officer at Finastra, points out:  "As I see it, there are three major misconceptions around RAG: 'RAG is a generative model' – This is not the case. RAG is a technique that combines retrieval and generation. 'RAG is always accurate' – While it does help improve accuracy of responses there can be errors in retrieval that lead to incorrect or misleading outputs. 'RAG can handle any query' – RAG can struggle with ambiguous, novel, or complex queries."

Another is the speed in which it can deliver such information that it draws from to ensure real-time data retrieval and maintain an accurate knowledge base. 

Maxime highlights: "Ensuring this real-time data retrieval without latency and maintaining a highly accurate knowledge base are significant hurdles."

This is a crucial point because the entire premise of RAG relies on providing relevant, up-to-date information to augment the language model's output. If there is significant latency in retrieving data from the knowledge base, it defeats the purpose of having a real-time retrieval component. 

However, advancements in technologies like Forward-Looking Active Retrieval Augmented Generation (FLARE) and Retrieval-Augmented Customization (REACT) are expected to address these challenges and further enhance RAG's capabilities.

As the adoption of GenAI in enterprises continues to grow, RAG represents a significant advancement in enhancing the accuracy, relevance, and reliability of AI-generated responses. 

By integrating LLMs with context-specific data, RAG systems can provide more informed and up-to-date answers, making them invaluable tools in various applications and industries. 

While challenges remain, the potential benefits of RAG, which will grow alongside advancements in new technologies, in improving GenAI systems are significant, and the technology is poised to play a crucial role in the capabilities of Gen AI systems.


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