Prompt Engineering Context Transcripts

Split Transcripts for AI — Meetings in Order, Decisions at the End

Hours of meetings exceed any message limit. Split transcripts at speaker-turn boundaries, deliver in order, and extract decisions only after every meeting has arrived.

Overview

Transcripts are deceptively large and structurally fragile: a decision's context lives in the turns before it, and a blind split severs exactly that. The package splits transcripts at paragraph and turn boundaries, keeps the meetings in chronological order, and holds the extraction task until the final chunk — because "every decision, owner, and open question" can only be answered across ALL the meetings, not per fragment. This setup loads a multi-meeting transcript in Analysis mode with exactly that extraction task waiting at the end.

How to use this resource

  1. Split at speaker turns

    Turn boundaries are paragraph boundaries — context stays attached to its discussion.

  2. Keep chronology

    Meetings arrive in order; cross-meeting threads stay followable.

  3. Extract across everything

    The final-chunk task spans all meetings — decisions reversed in week three stay reversed.

Why This Works

  • Turn-aligned splits preserve the context decisions depend on
  • Chronological delivery keeps multi-meeting threads intact
  • End-triggered extraction sees reversals and follow-ups blind fragments miss

Best for

  • Teams mining recurring meetings for decisions
  • Interview and research-call transcript analysis
  • Anyone whose transcript answers missed later meetings

Not for

  • Summarizing one meeting for humans — that's the Structured Summary Prompt's meeting mode
  • Continuing a chat about the meetings in a new session — that's the Context Handoff Builder

Use cases

  • Processing weeks of meeting transcripts at once
  • Keeping decisions attached to their discussion context
  • Extracting owners and open questions across meetings

FAQ

Why does the transcript splitter hold the extraction task until the last chunk instead of asking after each one?

Because the TASK line — "Extract every decision, owner, and open question across all meetings" — can only be answered across every meeting, not per fragment. The package holds it behind the "ALL 2 CHUNKS DELIVERED" marker, which tells the model to begin analysis drawing on EVERY chunk. Ask per-chunk and a decision reversed in a later meeting stays hidden.

How do I actually send the chunked meeting transcript to Claude without it answering too early?

Send Chunk 1 as one message, then wait for the model to reply ONLY "Received chunk 1 of 2" before sending Chunk 2 — the delivery rules require that acknowledgement and tell you not to edit the chunk contents. long-prompt-splitter formats those chunks and rules; you paste each into your own assistant in order, and only the final chunk releases the analysis.

Which splitting behavior keeps a decision from getting severed from the discussion that led to it?

Splitting happens at speaker-turn and paragraph boundaries, so the CHUNK STRATEGY reports "0 hard cuts" — no mid-turn breaks. Each timestamped line like "[14:04] Marco:" stays whole, and the two chunks arrive in chronological order. long-prompt-splitter reshapes where the cuts land; it does not summarize or analyze the turns it moves between chunks.

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