advocate-voice-tuning

Use when drafted comments miss the voice or the facts. Produces trained voice guidelines plus a focused knowledge base — the two inputs that turn generic AI comments into ones a teammate would sign.

Teach the voice by rewriting

Take a handful of real drafts (five is enough) and rewrite each the way you'd actually say it — or approve the ones that already sound right. Even 2–3 rewrites carry useful signal. The deltas between draft and rewrite become explicit voice guidelines: vocabulary ("uses" vs "leverages"), sentence rhythm, hedging style ("in my experience…" vs flat statements), how often and how directly affiliation gets disclosed, and comment structure (empathy-led vs question-led vs statement-led).

Do it well:

• rewrite instinctively — first-thought phrasing is the authentic signal, polished phrasing isn't
• rewrite from the Reddit post, not from the AI's draft
• include your natural disclosure style in the rewrites
• pick samples from varied subreddits so the guidelines generalize

Review the extracted guidelines afterward and hand-edit what the extraction missed. Sessions accumulate — rerun after off-brand batches, repositioning, or when spinning up a new persona.

Feed the facts

Voice without facts still produces wrong comments. Attach a knowledge base per campaign: product overview and differentiators, feature specifics, real use cases, pricing, FAQs, tone examples. The drafting engine retrieves only the chunks relevant to each thread, so focus beats volume — a tight 5-page knowledge base on one product outperforms a 100-page dump. Skip raw code, image-dependent decks, and internal ops docs (images aren't indexed — only surrounding text). Refresh it after every major release; stale knowledge produces confidently outdated drafts. One campaign per product keeps retrieval clean.