The emergence of generative AI models has turned the business world on its head. From public platforms like ChatGPT to private enterprise-grade Large Language Models (LLM), AI-powered platforms have the potential to transform the way businesses strategize, operate, and boost productivity and capabilities across a range of functions. But the AI journey is just beginning, and currently, generative AI is not without some challenges. The model can only provide information curated from numerous sources. It does not provide any value-added inputs, insights, or interpretations. More importantly, there is the challenge of AI hallucination when LLM engines provide incorrect information in response to queries. This can have serious consequences for businesses relying on AI to carry out critical functions.
Fortunately, solutions to address these challenges are already in play. Retrieval Augmented Generation (RAG) is a natural language processing (NLP) method that blends retrieval and generative AI models to address the challenges associated with LLMs. For organizations providing banking technology solutions, RAG holds significant potential in two key areas: customer support and knowledge management.
How it Works
What is the science behind RAG and how exactly does it work? An in-depth and extensive knowledge base or documentation library forms the foundation of RAG. It leverages this library to index keywords and put together semantic contexts into vector databases. It then draws upon this to generate quick, contextual, and meaningful responses. RAG is capable of collating current information from the external content library to provide up to date information. This differs significantly from LLMs that cannot reference current data and usually provide inputs that are slightly outdated.
By drawing upon external content databases, RAG systems are also capable of providing more comprehensive and contextual information. Both customer service and knowledge management use cases are based on the application of this knowledge base and RAG’s ability to understand context.
Transforming Customer Service
Customer service can make or break customer relationships. Studies indicate that 78 percent of people decide whether to continue buying from a brand based on their customer service experience.1 But what constitutes good customer support? When a customer reaches out to a brand helpline, most of the time they are not looking for mere information, but actual help to solve a problem. A comprehensive interaction that ends with their challenge being resolved is usually what makes customers happy.
It is important to state outright that as it stands today, RAG cannot fix issues. But it can improve troubleshooting and take AI-based customer support one step ahead of where it is today. RAG-based support chatbots can pull information from a wide range of information sources including product documentation, solution documentation, and customer-specific data to provide contextual inputs as well as precise guidance on where to look or how to address an issue.
These systems can be fed not just product documentation but the entire code base on the condition that it is well documented and structured. This helps the system to understand and retrieve contextual information from complex data sets like code and this is how RAG systems are better equipped to correctly narrow down the cause of a problem and help support teams and even the customer themselves diagnose issues quickly. It reduces human intervention and enables self-service by simplifying the troubleshooting process, cutting down time taken to identify issues and facilitating faster issue resolution.
The Roadmap to RAG Transition
Organizations intending to implement a RAG-based customer support must ideally do so in a phased manner with a hybrid system in the initial phases to ensure a smooth transition. They must ensure that the information feeding into the system is well structured and well documented because the system’s training time depends on the complexity and quality of the data provided. Most importantly, they must pilot the system internally, assess effectiveness with internal audiences, make any required corrections, and only then open it up to external customers. Maintaining the option of human support as a backup during the initial phases and during transition is also critical to ensure a seamless, hassle-free experience for customers. In future, as RAG-based systems mature, they have the potential of further elevating customer support by transforming the way websites work to offer personalized content based on the location of a user.
Improving Knowledge Management
Knowledge transfer and management is another area where RAG can be used. This function is often complex and has inefficient processes, especially when transitioning to new systems or architectures. Only a handful of personnel understand how the new systems work and they do not always have the time or the bandwidth to adequately train others. Traditionally, organizations created training videos that employees had to go through before taking mandatory assessments. This is a long, cumbersome process that does not always ensure complete knowledge transfer or help in on the job learning.
A RAG-powered training assistant based on vast repository of information, product, and code documentation can ensure a better knowledge management process by making information easier to find. Employees can ask questions and get immediate, accurate, and contextual responses, and the system can help guide them through the available information as well. Employees can connect with the RAG system continuously as needed, and over time, can absorb the material better. This process lends itself to the idea of continuous on-the-job learning as employees can quickly and easily access the information that they need without wasting time looking for it within the organization’s systems.
Conclusion
AI technologies have evolved exponentially in a short period of time. There are some challenges and gaps within models like LLM, but the good news is that improvements to address these gaps are already underway. RAG is designed to address the gaps within LLMs, and we are confident that it has the power to transform customer support and knowledge management. But these two use cases are just the beginning. RAG has the potential to transform how information is used, accessed, and managed across industries, unleashing a new era of operational efficiency and better user engagement.
