AI Personalization combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to create personalized and satisfying user experiences.
Wanted: Hyper AI Personalization at Scale
Let's chat about something exciting: Conversational AI!
We can’t turn on the TV, talk to our neighbor, or go to a party without talking about AI. It’s here to stay – and promises to be 10x what the Internet was in terms of disruption, innovation, and the way we live our lives. It will change every industry – from telecom, to healthcare, to government – and businesses of every size will be impacted. And we’re only at the beginning.
Chatbots have emerged as one of the first and most relevant AI personalization business tools used to help companies grow, scale, and provide incredible experiences to their customers. They engage with customers, patients, or subscribers, streamline operations, and enhance user experiences – among many other things. However, as technology advances and user expectations grow, the need for more sophisticated conversational AI solutions will be the next wave of innovation and a real game changer.
The traditional chatbot, while effective for answering basic questions, often lacks the ability to understand context, provide nuanced responses, or engage in truly meaningful conversations. It’s also subject to AI hallucination issues, that can lead to brand damage, legal liability, and regulatory risk.
Recent court cases show that companies can be held liable for AI outputs making it even more important to establish a strong data foundation for governance, security, and personalization – without having to have a human in the loop to supervise every interaction. Such chatbot limitations stem from data that’s misleading, outdated, inaccurate, and even toxically biased.
Retrieval-Augmented Generation Personalizes AI
Enter Retrieval-Augmented Generation (RAG), an innovative approach that combines the strengths of retrieval-based and generative AI models to enable contextual personalization. Real conversations require real-time responsiveness, so RAG must respond in real time – unlike many of the original frameworks that were built for analytics use.
At its core, RAG leverages Large Language Models (LLMs) trained on publicly available information, such as OpenAI's GPT, Anthropic Claude, Meta’s Llama and Google’s Gemini. Because they’re exposed to vast amounts of information, LLMs can generate responses that are not only fluent and coherent, but also highly effective in answering basic querstions. The problem is that LLMs are also subject to hallucinations, especially when having to deal with more complex or personalized interactions. Although retreival-augmented generation helps reduce false, illogical, or irrelevant responses, RAG hallucinations are still prevalent.
Get a condensed version of the Gartner RAG report on us.
Preventing Hallucinations with GenAI Data Fusion
K2view GenAI Data Fusion, a new approach designed to further reduce hallucinations and enable more accurate and personalized responses, grounds LLMs in structured and unstructured data retrieved from private, trusted company data sources.
GenAI Data Fusion is based on patented Micro-Database™ architecture that can scale to billions of individual records (customer, or any other business entity) providing real-time data in under 200ms which keeps things… well, conversational!
Imagine a RAG chatbot that can instantly access millions of real-time records for customer / product / finance / network / you name it, data!
The Bright Future of AI Personalization
Integrating real-time operational data into RAG conversational AI opens new possibilities for businesses across a wide range of industries and use cases. From retail and logistics to healthcare and hospitality, organizations can use active retrieval-augmented generation, powered by GenAI Data Fusion, to deliver hyper-personalized experiences and drive operational efficiency in real-time.
However, the integration of real-time operational AI data poses significant challenges, including data consistency, scalability, and security. It requires a robust data infrastructure capable of ingesting, processing, and analyzing vast amounts of data with minimal latency, as well as advanced techniques for data governance and privacy protection.
As we continue to push the boundaries of RAG AI technology, it is essential that we address these challenges and ensure that the benefits of real-time operational data are realized without compromising on data integrity or user privacy. By enriching RAG GenAI with real-time operational data, businesses can unlock new opportunities for growth, innovation, and customer engagement in an increasingly data-driven world.
The need to make conversational AI more relevant, timely, accurate, and safe is top of mind for leadership teams across the globe right now – and neccesitates an approach that can deliver highly relevant structured and unstructured data at enterprise scale with sub-second response times. An extended RAG tool is just the answer.
Discover the world’s premier RAG tool – AI Data Fusion by K2view.
