Data & AI

In part one of our five-part blog series exploring how to design a robust RAG workflow with the Progress Data Platform, we dive into GenAI and LLM fundamentals, the value of knowledge graphs and the purpose of content preparation and content discovery in the RAG workflow.

As the demand for intelligent and contextual business insights rises, knowledge managers need comprehensive semantic technologies to enhance their productivity and maximize the value of information. Semaphore 5.10.1 is here to help!

In today’s information-rich world, tapping into the most valuable knowledge within an organization can still be a challenge. It’s locked in the images of a product catalog, scattered across a multi-page table in a financial report, or split between diagrams and charts in a dense research paper. Standard extraction tools or basic RAG pipelines can only get you so far, often missing the nuance and context that’s critical for your business.

Retrieval Augmented Generation (RAG) has emerged as a powerful paradigm for grounding Large Language Models (LLMs) in factual, relevant information. However, the true power of RAG hinges on a critical element: sufficient context.