Fix broken STT models with Context Biasing

If you've ever dictated a prompt to a coding agent, you know how it goes. You say "Deno" and it hears "Dino." If you're adding dictation to an app, or building a voice agent or coding agent, one misheard library name or command can break the whole interaction. Your users won't blame the speech model. They'll blame your app. The fix is called context biasing. Most speech-to-text models already support it, but few developers know about it. In this video I'll explain why transcription models trip over technical words, show you how to set up context biasing in just a few lines of code. Links • Try the demo: https://context-biasing.conflare.workers.dev/ • Mistral context biasing docs: https://docs.mistral.ai/studio/audio/speech_to_text/offline_transcription • Mistral speech-to-text overview: https://docs.mistral.ai/studio/audio/speech_to_text