A Chinese developer shares their engineering journey to build a zero-hallucination QA system for an AI reading app. The key challenge was ensuring answers are strictly grounded in the original book text, with one-click traceability to specific paragraphs. The article describes three iterations: initial naive RAG, improved retrieval with chunking and reranking, and a final architecture that combines strict citation constraints with efficient indexing. The author emphasizes trade-offs between cost, latency, and accuracy, and provides practical tips for avoiding common pitfalls like hallucination from out-of-context retrieval. This is a must-read for engineers working on AI reading, document QA, or RAG applications, offering real-world experience rather than theoretical advice.
This article details a three-iteration engineering process to achieve zero-hallucination question answering in an AI reading app, where answers are strictly based on the book text and can be traced back to specific paragraphs. It offers valuable lessons for anyone building RAG or document QA systems, balancing cost, latency, and accuracy.