Do you want your AI SDK app to answer from your own docs?
We wrote about how that works in the blog post below:
- ZeroEntropy recently released zembed-1. We ran it through our embedding leaderboard - it's now #1! 0.946 NDCG@10. 55–80% win rate across 16 models. Impressive work by @ZeroEntropy_AI! Full breakdown below.
- We tested Claude Opus 4.6 for RAG. Key takeaways: - Best for for factual, doc-based Q&A - Clear upgrade over 4.5 on harder questions More context is in the blog:
- We tested different ways to detect hallucinations in RAG. LLM judges, atomic claims, encoder-based NLI. Each comes with clear trade-offs in accuracy, latency, and cost. Write-up + benchmarks:
- New: RAG prompt templates library. Constraint-first prompts for grounded answers, citations, uncertainty, and response length control. Copy, use, upvote/downvote what’s actually useful. 🔗👇
