Retrieval-augmented generation, or RAG, combines a generative model with a search step that supplies relevant information at request time. A system converts the query into a searchable representation, retrieves passages from approved sources, and includes them as context for the model’s response. This approach can provide current or organization-specific facts without retraining the model and can support citations. Quality depends on document preparation, indexing, permissions, retrieval ranking, context limits, and the model’s ability to use evidence correctly. RAG reduces some unsupported claims but does not eliminate them: sources may be outdated, relevant material may be missed, and the model may still distort or ignore retrieved information.
Opera today introduced Neon, an AI-powered browser that can run tasks and navigate pages autonomously — aiming to shift web browsing from passive to “agentic.”
OpenAI’s next version of its Sora video generator will default to including copyrighted content unless owners explicitly opt out – drawing criticism from media creators.
OpenAI introduces ChatGPT Pulse, a proactive feature delivering daily updates and suggestions directly in the app, positioning it as a potential challenger to social media feeds.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.