When do you fine-tune a reranker on your own data, and how do you build the training set?

An off-the-shelf cross-encoder is general; your domain carries jargon and relevance rules it never encountered. The signal is knowing when fine-tuning pays off, how to mine hard negatives, and how to avoid training a reranker that merely memorizes your retriever's mistakes.

Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.