Executive Summary Prompt
Summary, Why It Matters, What Happens Next — the executive summary contract for readers who will never open the source.
Build a prompt that turns a long article or report into faithful key points — compressing what is there, never adding outside knowledge or padding thin sections.
The most common summary request is also the one AI gets wrong most: "summarize this long document" usually returns either a vague paragraph or invented detail. This builds a prompt that compresses a long article or report into ordered key points, with fidelity rules that keep it to what the source actually says — no outside knowledge, no padded sections. Open it in the Structured Summary Prompt to adjust the structure, length, and how strictly it sticks to the source.
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Produce the output and paste it where you need it.
Its FIDELITY RULES do it: "Summarize only what appears in the source — do not add outside knowledge," plus "Never pad a thin section — a short faithful section beats a full invented one." Numbers, dates, and names must be preserved exactly as written. It outputs at most eight ## Key Points. The prompt enforces this on the model you run it in; you still spot-check the result.
Its QUOTE RULES are explicit: "Paraphrase everything — do not include direct quotes." The output is a compressed ## Key Points list, not an excerpt collection, and interpretation stays minimal and marked as interpretation when it appears. If you specifically need verbatim passages, this preset isn't it. Open it in the Structured Summary Prompt to adjust structure, length, and strictness before generating.
Summary, Why It Matters, What Happens Next — the executive summary contract for readers who will never open the source.
The blocks a reliable summary prompt needs: source guidance, a fixed section skeleton, length budgets, fidelity rules, and quote handling.
The fidelity ladder: Balanced, High, Strict — and the six-rule battery that keeps a summary inside its source.
The contract that stops AI documents from restructuring themselves: a pinned section skeleton, forced tables, and strict consistency rules.
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
'Make it good', 'be detailed', 'keep it interesting' — vague prompts get vague output. The fix is mechanical: replace every fuzzy word with a checkable instruction.
Build summary prompts with fixed sections, length caps, and no-invention fidelity rules.
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
A document too long for the model has to be split — but a blind split makes the AI forget earlier parts, drift on definitions, and lean on whatever it saw last. Here's how to split and synthesize without losing the thread.
AI summaries read clean but quietly distort — an exception dropped, a "may" turned to "will," a target read as a guarantee. Here's how to summarize a document faithfully: scope it, name what must be preserved, keep the caveats, and check it back against the text.