Question Answering for Multi-Release Systems: A Case Study at Ciena

Companies regularly have to contend with multi-release systems, where several versions of the same software are in operation simultaneously. Question answering over documents from multi-release systems poses challenges because different releases have distinct yet overlapping documentation. Motivated by the observed inaccuracy of state-of-the-art question-answering techniques on multi-release system documents, we propose QAMR, a chatbot designed to answer questions across multi-release system documentation. QAMR enhances traditional retrieval-augmented generation (RAG) to ensure accuracy in the face of highly similar yet distinct documentation for different releases. It achieves this through a novel combination of pre-processing, query rewriting, and context selection. In addition, QAMR employs a dual-chunking strategy to enable separately tuned chunk sizes for retrieval and answer generation, improving overall question-answering accuracy. We evaluate QAMR using a public software-engineering benchmark as well as a collection of real-world, multi-release system documents from our industry partner, Ciena. Our evaluation yields five main findings: (1) QAMR outperforms a baseline RAG-based chatbot, achieving an average answer correctness of 88.5% and an average retrieval accuracy of 90%, which correspond to improvements of 16.5% and 12%, respectively. (2) An ablation study shows that QAMR's mechanisms for handling multi-release documents directly improve answer accuracy. (3) Compared to its component-ablated variants, QAMR achieves a 19.6% average gain in answer correctness and a 14.0% average gain in retrieval accuracy over the best ablation. (4) QAMR reduces response time by 8% on average relative to the baseline. (5) The automatically computed accuracy metrics used in our evaluation strongly correlate with expert human assessments, validating the reliability of our methodology.

Parham Khamsepour, Mark Cole, Ish Ashraf, Sandeep Puri, Mehrdad Sabetzadeh, Shiva Nejati · 2026