Nour El Mawass

Processing documents with LLMs comes with unexpected challenges: handling long inputs, enforcing structured outputs, catching hallucinations, and recovering from partial failures.

Retrieval-augmented generation (RAG) has become a key application for large language models (LLMs), enhancing their responses with information from external databases. However, RAG systems are prone to errors, and their complexity has made evaluation a critical and challenging area.