Speech-to-text models keep guessing wrong on the words that actually matter. Brand names become common words. Medical terms get butchered. Project codes vanish. AssemblyAI Universal-3.5 Pro fixed this with contextual prompting. Feed it your domain context and it stops guessing. Here's how it works 🧵👇

How contextual prompting works: Before transcription, you feed the model domain context. Prior patient notes for clinical audio. Meeting agendas for session recordings. Product catalogs for call centers. The model uses that context to guide what it writes. Stops guessing on domain-specific terms.

The problem it solves: Real-world audio is messy. Short clips, background noise, domain terms that sound like everyday words. Most models treat every clip in isolation. Universal-3.5 Pro takes context and makes the transcript accurate where it counts.

Best use cases: → Clinical: feed prior-visit notes, medication lists → Meetings: drop in agendas or last session's notes → Call centers: supply product, brand, plan names upfront → Legal: case briefs and terminology guides → Any domain where precision on specific terms matters

Try it with your own audio (no build required): assemblyai.com/?utm_source=X&… Docs: assemblyai.com/docs?utm_sourc…