Tag: AI

Multi-hop retrieval runs several search passes in sequence to answer questions whose supporting evidence sits in separate documents. This post reviews what it is and how Progress Agentic RAG integrates multi-hop retrieval into its architecture.

AI has become everyone's favorite buzzword. It's on every slide deck, product page and conference keynote. Yet, somewhere between the hype cycles and headline claims, AI has started to mean everything and nothing at the same time.

This post compares ChatGPT to traditional retrieval-augmented generation (RAG) and the Progress Agentic RAG solution, highlighting the difference between AI tools built for personal use and those designed to serve as governed knowledge layers across an organization.

Deep research is iterative, not transactional. AI must preserve context, reasoning and evidence across long-running investigations to be useful in R&D and turn isolated insights into institutional advantage. Trust is the gating factor here—when outputs can’t be traced, reviewed or defended, AI stalls at the pilot stage and never reaches production. It's production-ready AI that compounds research value.

NotebookLM represents a promising shift in how courseware developers approach content creation—moving from manual drafting to strategic curation and refinement. Learn more.