technique

Dictation post-process

Dictation post-process is the LLM step that cleans raw transcription into polished text — adds punctuation + paragraphs, removes filler words, fixes grammar, expands abbreviations, applies user style. The reason modern dictation feels magical vs system dictation.

Raw Whisper transcription is good but raw — no smart paragraphing, occasional filler ('umm', 'you know'), inconsistent punctuation. Post-process pipes the raw text through an LLM with a prompt: 'clean this up, add punctuation + paragraphs, remove filler, preserve meaning'. Optional: domain-aware style prompts ('this is a Slack DM, keep casual'), per-app modes (formal email vs casual text vs commit message), custom vocab dictionaries for proper nouns. Wispr Flow / Superwhisper bake this in. Trade-offs: LLM can hallucinate words not actually said (especially proper nouns), latency adds ~500ms. The right mode for a given app is the difference between dictation feeling natural and feeling robotic.

When to use dictation post-process

Common mistakes

FAQ

What is dictation post-process?

Dictation post-process is the LLM step that cleans raw transcription into polished text — adds punctuation + paragraphs, removes filler words, fixes grammar, expands abbreviations, applies user style. The reason modern dictation feels magical vs system dictation.

When should I use dictation post-process?

Any production dictation app.

What are the most common mistakes with dictation post-process?

Skipping post-process — feels like 2010 dictation. Over-prompting — LLM rewrites too aggressively, loses user voice.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/dictation-postprocess.md.