Dictation Instructions

To fix speech-to-text errors in technical documentation, apply a project glossary to normalize technical terms, remove filler words, and preserve code tokens. This corrects dictated voice notes into polished, accurate technical text.

Yes, you can clean up dictated text without altering filenames and code tokens by using preservation rules. The system corrects speech-to-text errors and improves clarity while preserving code examples, filenames, and configuration syntax exactly as dictated.

To normalize voice-dictated workflow markdown for GitHub Agentic Workflows, apply a project-specific glossary to correct terminology, remove filler words, and favor active voice. This enforces consistent formatting expectations for gh-aw documentation.

Speech-to-text cleanup works effectively for voice-dictated README sections by applying glossary-driven corrections and clarity improvements. It removes ambiguous phrasing and shortens wordy phrases while retaining the intended technical meaning.

Limitations of using dictation cleanup for technical editing include the inability to generate or alter code examples, relying entirely on a provided project glossary for term normalization, and the strict avoidance of inventing configuration syntax beyond what was dictated.