How to Write LinkedIn Carousel Slide Text from a Whitepaper

Carousel prompts wander into caption engines: hooks, hashtags, agree? in the first comment. This job is on-slide text from a whitepaper. If a number is not in the paste, it does not go on a slide.

The matching generator is the LinkedIn Carousel Slide Text from a Whitepaper (Not a Caption Engine) (Linkedin Carousel Slide Text From A Whitepaper Not Captions) prompt. Browse related cards in the PromptDig library (Browse more prompts). When a filled run survives, share the version you actually use (Share a prompt).

Refuse the caption pack

Turn a whitepaper into LinkedIn carousel slide text only. Not a caption, hashtag, or thought-leadership engine. No invented stats. Start by filling Inputs, not by asking the model to remember last week's run. If a field is blank, write NONE or NOT IN INPUTS and leave it blank through Generate. The card is built so the model cannot honestly invent a number, owner, URL, or command that you did not paste.

Paste these fields before you hit run:

Whitepaper title and date: [Paper]
Pasted excerpts I may use: [Excerpt]
Audience on LinkedIn: [Audience]
Slide count I want: [Count]
Character budget per slide: [Budget or 140]
Brand words I may use: [Brand]
Claims I must not make: [Banned]
CTA on last slide (or NONE): [CTA]
Must-keep figures: [Figures]
Language: [Lang]

That inventory is the honesty ledger. Anything that does not appear there is forbidden in the draft. If you catch yourself adding a nice-to-have after the run, you are no longer using the card. You are ghostwriting. Put the extra fact in Inputs and run again.

One figure per slide, counted characters

Generate is numbered on purpose. Do not skip a step because the first paragraph looked done. The early steps exist to stop later prose from smuggling claims.

Walk the Generate list in order:

1. Scope lock: slide text only. No caption. No hashtags. No first-comment engine. LinkedIn 2026 document carousel.
2. Honesty ledger: every figure in Excerpt and Figures. Forbidden: any other number.
3. Slide outline (Count): job of each slide, source fragment.
4. On-slide copy per slide under Budget characters. Print counts. Lang.
5. Visual notes: layout only (title, 3 bullets, chart placeholder). No fake UI screenshots of LinkedIn.
6. Last slide CTA: only CTA. If NONE, end on a recap from Excerpt.
7. Cut list: sentences that would belong in a caption, not on a slide.
8. Compliance pass: quote Banned claims and invented stats. Cut them.

If a step asks for a version lock, quote the version from Inputs in the output. If a step asks for a refuse list, keep the refuse list in the published artifact, not in a sidebar you delete. Reviewers should see what the model was not allowed to do.

Layout notes, not fake LinkedIn UI

Most failures are the same shape: a missing field gets a confident fill. A conversion rate appears. A Gradle task appears. A flash point appears. A caption appears on a job that asked for slide text only. Your review is to search the draft for numbers, names, and commands, then grep Inputs. No match means cut.

Honor the constraints as hard stops, not vibes:

• Slide text only. Refuse if the user asks for a caption pack.
• Never invent a percentage, n=, or dollar figure.
• Do not add hashtags.
• Stay at Count slides.
• Not a thought-leadership post generator.

When the card says not legal advice, not certification, not an exam dump, or not a caption engine, that sentence belongs at the top of the output. Deleting it to look more finished is how you inherit risk.

Last slide is recap when CTA is NONE

Finish with the compliance pass the prompt already asks for. Quote the banned-word hits. Cut them. Print character counts when the job has a cap. Print word counts when the job has a budget. List gaps as gaps. Five missing facts are more useful than one smooth paragraph.

Tags on the card (linkedin carousel slide text, whitepaper to carousel slides, not a caption engine) are a reminder of the job shape, not an invitation to wander into a neighboring cluster. If you need a different surface, open a different PromptDig card rather than stretching this one.

Fill the card, then run

Replace every bracket. Run on ChatGPT, Claude, or Gemini. Read the ledger first, then the artifact. If the model invents a commit, KPI, DOI, PEL, bid, or logo, discard the run. Tighten Inputs. Run again. Share the filled card that survived, not the first draft that sounded done.