Ask NICE, get evidence: a high-precision RAG for UK guidelines
From 300 guidelines to precise answers—grounded, not guessed.
This RAG system uses a hybrid retriever over 10,195 chunks (from 300 NICE guidelines). Retrieval shines: MRR 0.814; 81% recall at first chunk and 99.1% within top 10 (7,901 queries). In generation (70 QA pairs), RAG lifted O4-Mini faithfulness by 64.7 points to 99.5%, and achieved perfect context precision (1), outperforming Meditron3-8B on faithfulness.
Why it matters: Grounded answers reduce hallucinations and speed evidence lookup.
It’s like a librarian who hands you the exact page, not just a summary. 📚🔎⚕️
See the retrieval pipeline and evaluation—then imagine clinic-ready tools built on top.
Paper: http://arxiv.org/abs/2510.02967v1
Register: https://www.AiFeta.com
Paper: http://arxiv.org/abs/2510.02967v1
Register: https://www.AiFeta.com
#RAG #HealthcareAI #EvidenceBased #NLP #InformationRetrieval #Guidelines #AIinMedicine
