When a customer needs help with a recent purchase, typically they start the conversation with the company’s chatbot. For the experience to be both relevant and positive, the entire exchange needs to be grounded in that customer’s data, such as their recent product purchase, warranty information, and any past conversations they have had. The chatbot should also be tapping into company data, such as the latest learnings from other customers who have bought similar products and internal knowledge base articles.
Some of this information might reside in transactional databases, structured information, while the rest might be in unstructured files, such as warranty contracts or knowledge base articles. Both types of data need to be accessed, and the right data needs to be utilised. If not, the exchange with the chatbot will be at best frustrating and at worst inaccurate.
An effective way of making LLMs more accurate is with an AI framework called Retrieval Augmented Generation. This enables companies to use their structured and unstructured proprietary data to make generative AI more contextual, timely, trusted, and relevant.
Combining all your customer data, structured and unstructured, into a combined 360-degree view will ensure customers have the most relevant information for any enterprise scenario.
Many companies are exploring the use of Retrieval Augmented Generation technology to improve internal processes and provide accurate and up-to-date information to advisors and other employees. Offering contextual assistance, ensuring personalised support, and continuously learning will improve efficiency decision-making across their organisation.
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