ChatGPT Prompt Engineering: Advanced Data Analysis for Writing

TL;DR

Consolidate external research into a single plain-text notes file before prompting.

Briefing Cornell Notes

Briefing

A repeatable workflow turns uploaded research notes into a polished, multi-section blog post by chaining OpenAI’s Jack GPT code interpreter with a set of custom “outline → introduction → section-by-section writing” prompts. The core payoff is control: the model produces structured drafts that follow a chosen outline, then expands each section in a consistent first-person voice—while allowing the writer to steer tone, pacing, and stylistic variation.

The process starts with gathering raw material. For a topic like “AGI existential risk,” the workflow pulls notes from external sources, then consolidates them into a plain-text file. Those notes are uploaded into the code interpreter, where an “outline prompt” instructs the model to read the uploaded content, reflect on it, and generate a blog outline. The prompt also includes a style direction via a system-like instruction: the writing should be in the voice of a specific admired author (named in the prompt), with expertise framed around AI, human psychology, and philosophy. The resulting outline is detailed enough to include an introduction, historical context, perspectives (including “doomers”), and philosophical interludes.

Next, the outline becomes a scaffold for drafting. The writer saves the outline into a text file, then runs an “introduction prompt” that explicitly requires a hook from the first lines. The introduction prompt keeps the first-person stance (“I think,” “I believe,” and similar phrasing), demands stylistic imitation without naming the author in the final text, and emphasizes lexical variety—avoiding repeated phrases and terms. The output introduction is saved as its own file.

Then the workflow iterates through the outline. For each section—starting with “historical context”—the model is prompted to read the full text written so far, then generate the next section according to the outline. After each run, the newly written text is saved and re-uploaded so the model can maintain continuity. The transcript highlights that this section-by-section approach yields writing described as “vibrant and alive,” with concrete references embedded in the historical discussion, including atomic-bomb-era imagery (“I have become death to destroyer of worlds”), references to J. Robert Oppenheimer, Alan Turing, and “Hana ornament” (as written in the transcript).

By the end of the chain, the article lands at roughly 1,700 words (about 10,000 characters). The creator notes that the draft could be expanded but chooses a length that feels “perfect” after a quick read-through. The workflow concludes with sharing the prompts and the resulting text for others to try, including a link to a podcast-style audio version. Overall, the method matters because it replaces one-shot prompting with a controlled drafting pipeline that turns messy notes into a coherent, stylistically consistent long-form piece.

Cornell Notes

The workflow uses Jack GPT code interpreter to convert uploaded research notes into a full blog post through a staged prompting pipeline. First, notes are consolidated into a plain-text file and uploaded to generate a detailed outline. Next, an introduction prompt writes a first-person hook with strict style constraints: imitate an admired author’s vibe without naming them and vary wording to avoid repetition. Finally, the process repeats section-by-section (e.g., historical context), with each new run reading the full draft so far to maintain continuity. The result is a coherent long-form article (about 1,700 words) that embeds specific historical and philosophical references while keeping a consistent voice.

How does the workflow turn raw research notes into a structured writing plan?

It collects notes on the target topic (e.g., AGI existential risk), pastes them into a plain-text file, uploads that file into Jack GPT code interpreter, and runs an “outline prompt.” The prompt instructs the model to read the uploaded notes, reflect on them, and produce a blog outline with sections such as introduction, historical context, multiple perspectives (including “doomers”), and philosophical interludes.

What constraints shape the introduction so it sounds consistent and engaging?

The introduction prompt requires a first-person voice using phrasing like “I think” and “I believe.” It also demands a hook early, uses thought-provoking questions, and enforces style imitation without mentioning the author’s name in the final text. A key constraint is lexical variation: it explicitly warns against repeating the same phrases and terms, instructing the model to use different words and expressions.

Why does the workflow save and re-upload text between sections?

Each section-writing step asks the model to read the full text written so far before generating the next section. Saving the current draft (introduction, then introduction+historical context, etc.) and re-uploading it helps maintain continuity—tone, references, and narrative flow—rather than producing disconnected chunks.

What kinds of references appear in the historical context section?

The transcript points to concrete historical and philosophical references, including J. Robert Oppenheimer and the atomic-bomb test context, the line “I have become death to destroyer of worlds,” and references to Alan Turing. It also mentions “Hana ornament” as part of the historical discussion, as written in the transcript.

What is the final output length and how is it judged?

The completed article is described as about 1,700 words (roughly 10,000 characters). After a quick read-through, the creator says it could be expanded but feels “perfect” at that length, suggesting the workflow is tuned for completeness without unnecessary bloat.

Review Questions

What prompt steps are used to move from notes to an outline, then from an outline to an introduction, and finally from an introduction to subsequent sections?
Which specific instructions in the introduction prompt enforce first-person voice and prevent repetitive phrasing?
How does re-uploading the draft after each section affect continuity in the final article?

Key Points

1
Consolidate external research into a single plain-text notes file before prompting.
2
Generate a detailed outline first, then treat it as the blueprint for drafting.
3
Use a dedicated introduction prompt that enforces a first-person hook and lexical variety.
4
Write sections iteratively: save the current draft, re-upload it, and ask for the next outline section.
5
Style control comes from system-like instructions that imitate a target author’s vibe without naming them in the output.
6
Section-by-section prompting helps maintain continuity and coherence across a long-form piece.
7
The workflow can produce a complete draft around 1,700 words, with room to expand if desired.

Highlights

The method chains prompts in stages—outline, introduction, then section-by-section expansion—rather than relying on a single one-shot request.

First-person voice is enforced with explicit language patterns (“I think,” “I believe”) and a requirement to vary wording to avoid repetition.

Continuity is maintained by re-uploading the full draft so far before generating each next section.

The historical context section incorporates recognizable references tied to atomic-era history and figures like J. Robert Oppenheimer and Alan Turing.

Topics

Prompt Engineering
Code Interpreter
Long-Form Writing
AGI Risk
Style Constraints

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