Prepare Transcripts for AI Analysis — Speakers Stay Attributed
Transcript analysis fails when speakers blur. Transcript mode packages the conversation with attribution rules: who said it, when it was revised, and no words in anyone's mouth.
Overview
Transcripts carry meaning in their structure: who said what, and in what order. Pasted raw, that structure decays — models merge speakers into one consensus voice and treat early positions as final. Transcript mode packages the conversation with the rules that preserve it: every claim attributed to its speaker with a section citation, chronology respected (later statements override earlier ones, citing both), and implied-but-unstated positions labeled as inference, never attributed. This setup loads a decision-making meeting where exactly those rules earn their keep.
Workflow
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Keep the speakers visible
The transcript travels verbatim — labels and timestamps intact for the attribution rules to use.
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Enforce attribution
"X said…" with a citation — never an unattributed "it was said".
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Respect revisions
When someone changes position, the later statement governs — and both get cited.
Why This Works
- Attribution rules prevent the consensus-voice blur that ruins transcript analysis
- Chronology rules handle the positions that evolved mid-meeting
- Inference labeling keeps unspoken positions out of people's mouths
Best for
- Meeting, interview, and call transcripts
- Analyses where attribution matters legally or politically
- Multi-speaker recordings with evolving positions
Not for
- Splitting hours of transcripts that exceed the window — the Long Prompt Splitter's transcript mode
- Carrying the meeting's decisions into a new session — the Context Handoff Builder
Use cases
- Analyzing meetings without speaker blur
- Keeping who-said-what answerable
- Handling positions that changed mid-conversation