Tag: RAG

This blogs argues that the success of a retrieval-augmented generation (RAG) system depends more on data quality, metadata and governance than on model tuning or pipeline optimization. Without clear metadata, document ownership, permissions and freshness controls, AI systems can retrieve outdated or incorrect information, leading to hallucinations. Ultimately, trustworthy AI requires well-structured, governed data, not just more advanced models.

Here are some practical workflows for integrating personalization into your CMS based on your own data with Progress Agentic RAG.

Retrieval is the foundation that determines what AI can reason over and what it can’t. This resource explains why retrieval strategy, not model choice, is the key to reducing hallucinations, preserving context, and delivering trustworthy enterprise AI answers.

In the quest for leveraging data insights without exposing proprietary information, many companies are implementing RAG systems to query like an internal ChatGPT.

The data you need is already stored in your CMS. Progress Agentic RAG helps you connect this intelligence to action. These three workflows show how.