What’s keeping you from realizing the full potential of generative AI? Your data believe it or not! Learn how to turn your enterprise data into GenAI data.
From hallucination to precision: Better GenAI with RAG
Generative AI (GenAI) is a game-changer for customer-centric companies. Those that can leverage Large Language Models (LLMs) effectively are likely to operate more efficiently and competitively. Case in point, generative AI adoption is breaking all records.
Source: Gartner
One of the biggest obstacles to deploying a customer-facing generative AI app is the reliability of the LLM responses – with a false or misleading answer commonly referred to as an AI hallucination.
Retrieval-Augmented Generation (RAG) is a generative AI framework that has emerged to address this issue, among others. RAG integrates your private data with the publicly available information your LLM has been trained on. The results are more accurate and reliable responses, enhanced customer personalization and intimacy, and significantly fewer hallucinations.
Source: AWS
According to the AWS “CDO Agenda 2024: Navigating Data and Generative AI Frontiers” report, among the top challenges preventing organizations from realizing the potential of generative AI are insufficient data quality, data security, and data infrastructure. If that’s the case, what’s wrong with your existing data, and how can you fix it?
What’s wrong with your enterprise data?
Enterprise data isn’t ready for RAG GenAI consumption because:
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It’s spread across multiple systems
Your organization’s data is typically fragmented across dozens of enterprise systems, such as CRM, customer service, sales, and billing. For instance, providing your LLM with a complete and current view of the customer – while that customer is interacting with a RAG chatbot – requires real-time data integration, master data management, and data quality governance.
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Real-time access is a must
If, for example, the data for each of your 73 million customers is stored in a central data warehouse or data lake, how can you access all of Casey Garcia’s data in less than a second?
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Stringent privacy and access controls must be applied
To ensure your sensitive data remains private and secure, user and LLM access must be limited to authorized data only. As per the example above, that means only Casey’s data and nobody else’s. In addition, all Personally Identifiable Information (PII) should appear as masked or anonymized.
You’d also need to consider:
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Scaling the data infrastructure to support generative AI apps
If all your 48 million customers want to interact with your company chatbot at the same time, could your data infrastructure handle it?
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Controlling your generative AI costs
At enterprise scale, the cost of building and maintaining GenAI apps plays a critical part in the project’s success. Can you predict the number of LLM API calls required to deal with enterprise-scale GenAI data?
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Ramping up your prompt engineering expertise
Sophisticated prompt engineering capabilities are needed to generate smart contextual prompts, such as chain-of-thought prompting, to inject the relevant data into the LLM in a way that produces the most meaningful and relevant responses.
Realizing the potential of company data in GenAI
Despite these challenges, your private company data promises to keep your generative AI apps much more honest. When enterprise data is GenAI-ready and used by RAG, you can turn your generic LLM into a model knows your business, and more importantly, your customers.
As GenAI evolves, the need to integrate enterprise data continues to be top of mind for leadership teams across the globe. Companies are looking for ways to ensure their data is:
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Up-to-date and complete
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Accessed in real time at massive scale
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Secured and governed
Learn more about K2view GenAI Data Fusion, the RAG tool that unlocks enterprise data for generative AI.
