What Actually Works When Prompting AI to Build Outlines

What Actually Works When Prompting AI to Build Outlines

ByUS Stacked

1. Prompt templates that generate logical structure not fluff

There’s a difference between asking ChatGPT to “outline a blog post” and handing it a real prompt framework. When I just say, “give me an outline about password managers,” I’ll get five generic H2s ending in “Conclusion” — guaranteed. But when I feed it a templated structure — with constraints like max H2 character counts, no colons, uniform numbering — suddenly it behaves like a mildly competent editor under pressure. Not great, but usable.

What finally worked was formatting the template like this:

Generate X numbered H2 sections like:
1. Use cases for password manager sync-in-browser
2. How vault data gets encrypted during migration
…
- All titles 7–13 words
- No punctuation
- No emojis

That specific phrasing somehow avoids two classic GPT screwups:

  • Doesn’t think I’m asking for metaphors or emotionally vague headings like “A Digital Lock to Secure Our Minds” (bleh)
  • Doesn’t mix Markdown list and HTML header formats — happens way more often than expected

By the way, if you tell GPT “Make them image-filename safe,” it’ll remove punctuation, but multiple versions will randomly sneak in a forward slash. And you’ll get titles like “3. How Your-Pins Change-Over-Time/Best Tips” — so yeah, still needs cleanup.

2. The one setting that stops runaway heading inflation

Any time I let ChatGPT decide the number of sections, it goes wild. Like, 15 H2s for a post that should have six. It’s like it enters listicle mode and can’t stop itself. The fix? Be specific about physical formatting limits, not content metaphors.

Prompt test: “Generate exactly 7 H2 sections, each 7–13 words. Do not include bullet lists or nested headers.” → GPT3.5 gave me 21 sections. GPT-4 got it right on try two.

One surprise: If you mention a stricter total word count, it pays attention to section volume better. For example, saying “Maximum 1500 words” before asking for sections reduces bloat without killing detail — kind of like preemptively rate-limiting a webhook that thinks it’s helpful.

But the real trick? List the subformats first: numbered H2s, no punctuation, character range. THEN give a topic. GPT responds better when it thinks it’s solving for form-first, not writing.

3. Why your fourth H2 is always vague and lazy

This is weirdly consistent across AI responses: the fourth H2 tends to repeat earlier phrasing or introduce an abstract category that doesn’t belong. Like if sections 1–3 are tactical setup, config, and error handling… boom, section 4 is suddenly “Best Practices for Secure Collaboration.” No context shift, it just slips in like it was auto-generated via tag suggestions.

When I ran 20 test prompts to generate outlines around Airtable automations, 14 of them had trouble by H2 #4. The titles got longer, or they borrowed half the words from H2 #2. It was like the model forgot what it was doing and guessed wrong. So now, when templating, I add this line mid-prompt: “Avoid similar phrasing across sections, especially headings 3 and 4.” That’s nailed the issue maybe 70 percent of the time.

Also worth noting: GPT occasionally drops numbers entirely if it’s confused about block structure. So you’ll get:

1. Initial trigger for the automation
2. Filtering inputs before record lookup
Use case examples for dynamic delay

No fix for that besides rerolling or using code blocks to force format — and even then, you risk it hallucinating Markdown stylings back in.

4. ChatGPT unusably overreacts to the word overview

I tried using “overview” in prompt templates to indicate, like, “just give me a top-level structure, no deeply nested flows yet.” Big mistake. GPT will turn that into a generic Quarter-1-planning whiteboard summary with phrases like “enhancing team synergy” or “covering a range of key topics.” It treats “overview” like a license to skip all specificity.

Even in tightly-scoped prompts like: “Generate a blog post outline (H2s only, numbered, 7–13 words) overviewing the process of prompt chaining with GPT functions” — it floods me with titles like:

  • Understanding the Basics of Prompt Engineering
  • An Overview of GPT’s Advanced Capabilities
  • Exploring AI Use Cases Across Business Sectors

Now I ban the word “overview” completely in prompt templates. Instead, I use “structure,” “outline,” or just say “organized topics.” Much more direct responses from GPT when you keep the language task-oriented (structure) versus content-themed (overview).

5. Token-friendly formatting that avoids truncation traps

When using API access to generate outlines (especially with GPT4-32k), chunking matters. A lot. If your prompt includes multiple numbered instruction bullets plus format examples, it can push useful content too far down the token list — and that’s where truncation kicks in silently. You’ll think it just flaked, but it ran out of breath.

Found this out the hard way while working inside a Zapier OpenAI action. My input prompt had 8 numbered constraints followed by a topic, and section #6 was always suspiciously generic. Checked the logs — it never finished that part. It just dropped character formatting near the 3050-token mark out of nowhere:

...6. How to inject conditional steps in command flows
7

Total silence after that. No section seven. No formatting error. The webhook ran anyway.

So now I do two things:

  • Keep format examples extremely short (2 lines max)
  • Move topic context above all instruction blocks when possible

You could also stream completions and detect sequencing errors (e.g., missing section number jumps), but that’s overkill unless you’re running multiple blog generators at once — which I did for like a week before hitting limits on the Zapier side.

6. Forced repetition logic that tricks the model into staying useful

This was a bit of a hack, but I discovered that inserting fake-but-structurally-similar prior outputs into a prompt keeps GPT more disciplined. So if you do something like:

Here is a correct example:
1. Benefits of collaborative prompt craft in team workflows
2. Techniques for batching prompt tests across use cases
3. Observing function token bleed in chain executions

Now recreate this structure for the topic: Notion formulas

…the result ends up tighter. It doesn’t repeat verbs as often. It avoids mid-outline drift because it thinks it’s matching a previous pattern. I even left a typo in one of the demo lines once (“craetive joining workflows”) and GPT matched the typo style — that’s how strong the mimicry drive is.

I use this trick the most when feeding it multiple post outlines back-to-back via API. Especially useful when the topics are adjacent but not identical — like scripting Notion widgets vs templating Obsidian commands.

7. Handling when the model weirdly forgets numbering rules

This one drove me nuts: sometimes GPT just plainly ignores the “use numbered H2s” rule midway. No warning, no explanation — H2s 1 through 4 are numbered, then it switches to unnumbered, or to Markdown bullets. Same model (gpt-4), same token window, same behavior across days. Seen it in both chat and API completions.

The partial fix is using triple backtick code blocks around your own prompt examples. This seems to push GPT into respect-for-format mode (since it thinks it’s inside playground-mode), e.g.:

```
1. When Notion formulas exceed 1000 character limits
2. How nested IF statements break conditional display
3. Fixing timestamp conversions between rollup sources
```

The irony is that the model adds Markdown syntax like ## sometimes after this — so it’s not a perfect fix. But it does force structure better than plain-text guidelines. Worth noting: if you use the word “heading” instead of “H2” in the prompt, GPT often gets confused and injects H3s or makes every section title wrapped in bold. Always say “H2” and always pair it with a phrasing reminder: “Numbered with digits like 1. 2. 3.” Otherwise, expect format slip on re-runs even if the first result looks clean.