Package Long Documents for AI — Delimiters and § Labels
Pasting a document raw mixes material with instructions. Package it: explicit delimiters, citable [§N] section labels, and grounding rules — the source travels verbatim.
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.
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.
Keep the speakers visible
The transcript travels verbatim — labels and timestamps intact for the attribution rules to use.
Enforce attribution
"X said…" with a citation — never an unattributed "it was said".
Respect revisions
When someone changes position, the later statement governs — and both get cited.
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.
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.
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.
Pasting a document raw mixes material with instructions. Package it: explicit delimiters, citable [§N] section labels, and grounding rules — the source travels verbatim.
A grounded workflow starts with a grounded source: package the reference once with strict rules, then run every question of the session against it.
Raw notes are fragments, not conclusions. Package them so the AI organizes without inflating — a four-word note stays an observation, not a firm claim.
Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.
The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.
Formats fuzzy agent instructions into a structured prompt with objective, available tools, constraints, success criteria, and failure handling.
Package source material with delimiters, citable section labels, and grounding rules — material and instructions stay separate.