Retrieval Augmented Generation (RAG) for the Workplace

We’re familiar with RAG – but which RAG is the right fit?

As organizations increasingly rely on AI-driven information retrieval and generation, traditional Retrieval Augmented Generation (RAG) systems often fall short in production environments. Various solutions have emerged to address this challenge, recognizing that “one size does not fit all” – each organization has unique requirements to tackle this issue within their framework.

Increase efficiency by up to 20% with Semantic RAG (AKA Graph RAG)

This webinar explores an innovative solution: Semantic RAG, which harnesses the power of knowledge graphs, large language models (LLMs), and graph databases to enhance semantic retrieval and generation capabilities.

A key focus will be on LLM-derived knowledge graphs, demonstrating how they can be created and refined with human-in-the-loop processes to achieve greater accuracy and explainability. We’ll explore memory constructs in Graph RAG operations, illustrated through practical demonstrations using PoolParty and GraphDB.

We will dive into a real-world case study and explore the pipeline, architecture, and practical applications of Semantic RAG systems. This will showcase valuable insights into the seamless collaboration between PoolParty and GraphDB to deliver a robust Semantic RAG solution. Expert strategies will be shared for maximizing the impact of these innovative technologies by shortening time to insight, providing savvy querying for the untrained, and lowering long term maintenance costs.

Marcia Ferreira, Data and Knowledge Engineer (Technical Consultant) at Semantic Web Company, and Krasimira Bozhonova, Solutions Architect at Ontotext, will walk you through the following aspects and explain the benefits of a Semantic RAG approach:

Watch our webinar to dive into details of Semantic RAG, such as:

• Vector RAG vs. Graph RAG
• Grounding LLMs with Graph
• Conventional RAG vs. Semantic RAG
• Different varieties of Graph RAG
• Demo of PoolParty Semantic RAG and GraphDB

In the meantime, have a look at our Semantic Retrieval Augmented Generation page to learn more about the capabilities and benefits of utilizing Knowledge Graphs to power your Generative AI experience.

We hope to see you there!