Generative AI Powered by Knowledge Graphs

What is Retrieval Augmented Generation (RAG)?

There has been a lot of buzz about the capabilities of Large Language Models (LLMs). We have witnessed instances where the outcomes produced by LLMs are impressive, or at the very least, sound impressive, while at other times, the results may fall short. However, relying solely on an open-source LLM poses a few challenges – the information provided may be inaccurate, outdated, and lack clear sourcing. Retrieval Augemented Generation, or RAG, is a framework designed to enhance LLMs’ accuracy, relevance, and traceability by supplementing user-generated prompts with primary source data.

Leveraging Semantic Technology to enhance the capabilities of RAG

As we have seen, the possibilities for hallucinations (creation of false or misleading information)  in LLMs is high. When looking at Generative AI in content creation, this defeats the purpose to assist the knowledge user in content creation by providing quick and reliable information. Worse yet, this poses a serious risk of disseminating inaccurate information in critical sectors such as pharmaceuticals, insurance, and health & safety, where even a single piece of incorrect data can have disastrous consequences. 

Unlocking the true potential of Generative AI hinges on building AI you can rely on, or “AI you trust”. This ushers the need for combining LLMs with a myriad of tools and AI methodologies to develop trustworthy Generative AI solutions. Enter Semantic Retrieval Augmented Generation or Semantic RAG – a specialized technique in Advanced Retrieval Augmented Generation that leverages Semantic technology to seamlessly fuse knowledge models and retrieved data during the content generation process.

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

• Overcoming the limits of LLMs
• Combining LLMs with other tools to create “AI you trust.”
• Building Generative AI on a strong Semantic base
• Developing a Domain Knowledge Model
• Semantic Search and Vector Search in Generative AI

Following this 30-minute webinar we will have an interactive question and answer period in which you can ask Andreas Blumauer any questions you may have about Retrieval Augmented Generation (RAG), Semantic RAG, and Generative AI.