Prompt Engineering Context Transcripts

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.

How to use this resource

  1. Keep the speakers visible

    The transcript travels verbatim — labels and timestamps intact for the attribution rules to use.

  2. Enforce attribution

    "X said…" with a citation — never an unattributed "it was said".

  3. 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

FAQ

How does Transcript Mode keep the AI from merging speakers into one consensus voice?

It writes an ANALYSIS RULE into the packaged prompt that bars merging different speakers' positions into one view, plus a grounding rule tying every claim to its speaker with the section's [§N] marker. So when the AI reports who backed cohorts, it attributes that to Femi at [§1] rather than an unsourced 'the team agreed'. The rules ship in the prompt you run wherever you paste it.

Will the AI report everything discussed, or only the decisions the meeting actually reached?

Only the decisions, held apart from talk. An ANALYSIS RULE tells the AI to distinguish decisions made IN the conversation from topics merely discussed, and the TASK asks it to extract the decisions plus who owns each follow-up. So Priya's write-up for finance before Thursday lands as an owned action, while legal still needing to see the consent copy stays flagged as discussed, not decided.

The tool auto-detected my file as a Transcript — what if that classification is wrong?

Override it in the Long Input Formatter before you run the prompt. The AI CONSUMPTION NOTES flag that the source type was auto-detected and tell you to reclassify in the tool if the classification looks wrong; the mode you pick decides whether the speaker-attribution and chronology rules get written in at all. Reselect the mode rather than hand-editing the generated prompt.

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