Last mentioned: Mar 10, 2026
As Large Language Models become central to enterprise workflows, the persistent issue of 'hallucinations'—plausible but false outputs—remains a critical barrier to adoption. This briefing explores the technical roots of AI inaccuracy and the emerging frameworks, such as Retrieval-Augmented Generation, designed to anchor models in verifiable facts.
This page surfaces every story mentioning Retrieval-Augmented Generation (RAG) across our ai coverage. We track each entity's appearance over time so readers can trace how the narrative evolves — which developments are isolated incidents, which build into longer arcs, and which reframe how operators in the space think about the entity. Story selection uses the same multi-source verification gate applied across the rest of our coverage.
Read our editorial methodology for how we identify, deduplicate, and score entity references. Our glossary defines the technical terms used across stories on this page, and our trends index contextualizes individual developments against the longer-running ai beat. Cross-entity comparisons live on our compare view.
| What you see | What it tells you |
|---|---|
| Story count | Number of distinct stories where Retrieval-Augmented Generation (RAG) was a primary or referenced actor. |
| Recency clustering | Whether mentions are concentrated in a recent window (a news cycle) or distributed (a sustained arc). |
| Sentiment distribution | Aggregate sentiment of the stories mentioning this entity, weighted by impact score. |
| Cross-niche links | When the same entity surfaces in our sibling networks, we link to those views to enrich context. |