SMRTR summary
RAG supercharges language models by tapping into external data on the fly, using vector searches to grab relevant info and blend it with prompts, resulting in smarter AI outputs.
SMRTR provides this summary for quick context. The original article belongs to The New Stack.
Read the original articleThis archive is built from SMRTR newsletter stories. Subscribe for hand-picked stories without the extra noise.
Wharton researchers found that people accept incorrect AI outputs 80% of the time—a pattern they call "cognitive surrender." For engineers and architects, this blind trust risks...
Dactyl by Deno is a browser-based tool that converts plain text into native iOS and Android apps—no Mac, Xcode, or coding needed. At $20/month, apps can be previewed instantly,...
Pick AI video models based on your shot's needs—duration, lip-sync, reference images, and true cost including failed generations—not leaderboard rankings. Sora 2's shutdown...
Only 5% of employees are truly AI fluent, according to Google and Ipsos research—meaning most workers lack the judgment to evaluate AI output or recognize when human...
AI freeing humans from routine work sounds promising, but without new sources of meaning, that space may fill with distraction and division. Technology won't decide the...
Agentic RAG systems need a multi-dimensional search budget (queries, tokens, time, cost) and a claim-based evidence ledger to prevent endless loops. Retrieval stops when evidence...