A developer attempted to build a Retrieval-Augmented Generation (RAG) pipeline for large books, initially using NVIDIA's nemotron 3 embed 1b model and Qdrant Cloud for vector embeddings. While this approach worked well for specific question-answering tasks, it failed when attempting to summarize entire books, often omitting crucial details or losing narrative flow. The developer discovered that the issue was not token limits but the AI
RANK_REASON [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
