Artificial Intelligence and Large Language Models (LLMs) are enabling many opportunities in modern data organizations. An up-and-coming application for LLMs is searching and interacting with your proprietary data.

Many enterprises have turned to Retrieval Augmenting Generation (RAG) to improve the out of the box reasoning of LLMs. This method uses your organization’s specific documents and data to find relevant information, rather than relying solely on what the AI was originally trained on.

However, while traditional RAG implementations have shown promise, they often struggle with complex reasoning, maintaining context across multiple documents, and handling interconnected information.

Are you looking to unlock the additional benefits of LLMs on your own proprietary data in a secure, reliable, and scalable way? Have you started a proof of concept but haven’t achieved the results you were hoping for?

Join us on December 11th when our Data & AI Consultants will demonstrate practical approaches that work to address some of these common limitations — without requiring a complete overhaul of your existing infrastructure. These powerful methodologies will help your organization achieve better business results from your AI systems.

What to expect:

•  Analysis of existing RAG systems and their limitations for your business
• Introduction to knowledge graphs and how they can enhance RAG implementations
• Strategies for improving AI response accuracy and reasoning capabilities
• Advice on what to consider when building out an AI system in your organization

Is it for you?

• Are you looking to improve your organization’s AI capabilities beyond basic document retrieval?
• Are your AI systems giving poor responses based off your internal data?
• Would you like to better understand which AI methodologies are right for your situation?