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About this tool
Splits a target word count across intro, H2 sections, FAQs and conclusion, then writes the outline prompt with that budget built in.
The Blog Outline Prompt Builder converts a target article length into an explicit word budget per outline block — introduction, H2 sections, H3 subsections, FAQ and conclusion — and writes an AI outline prompt with that budget embedded. It is built for content writers and SEO teams who want an outline that actually fits the word count they were briefed, using a 10% introduction share, an 8% conclusion share and the 238 words-per-minute average silent reading rate from Brysbaert's 2019 meta-analysis to estimate reading time.
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Every H2 and H3 in the prompt carries its own word allocation, so the drafted article lands near the target length instead of overshooting.
The tool flags sections thinner than about 120 words or fatter than about 500, before you waste a generation on an unbalanced outline.
Choosing informational, how-to, comparison, opinion or list intent swaps in a structural directive written for that reader, not generic filler.
A workable range is roughly 120 to 500 words per H2 section: below about 120 words a section cannot develop a point, and above about 500 readers lose the thread. This tool divides your body word budget evenly across sections and warns when the result falls outside that range.
Reading time is the total word count divided by 238 words per minute, the mean silent reading rate for English non-fiction found in Brysbaert's 2019 meta-analysis of 190 reading-rate studies. A 1,500-word article therefore reads in about 6 minutes.
A single total lets the model distribute words however it likes, which usually inflates the introduction and starves the later sections. Handing it a per-block budget — for example 150 words of introduction, five 180-word sections, a 240-word FAQ and a 120-word conclusion for a 1,500-word piece — constrains the plan so the draft fits the brief.
The generated prompt instructs the model to write [source needed] wherever evidence is required rather than fabricate figures, studies or quotations. You still need to verify and fill those markers yourself before publishing — no prompt makes model output trustworthy on its own.