Whisper out of the box is impressive, but anyone who's actually shipped it into production knows the pain: it fumbles proper nouns, product names, medical terms, legal jargon, and the entire long tail of domain vocabulary that matters most to your users. This is Part 6 of the series on turning OpenAI's Whisper into a genuine in-house ASR service, and it tackles that exact problem head-on — how to make the model reliably recognize named entities and domain-specific terms without having to retrain the whole thing from scratch.

The episode walks through practical techniques for biasing recognition toward custom vocabularies, integrating entity lists into the decoding pass, and closing the accuracy gap between a generic foundation model and a system that actually understands your domain's jargon. It builds on the earlier parts of the series covering overview, ROI analysis, long-audio and streaming handling, postprocessing, external language modeling, and timestamp accuracy, so it slots cleanly into a full production-ready pipeline. If you're evaluating whether to stick with a cloud ASR API or roll your own on top of Whisper, this is the piece that answers the vocabulary question.