Long LLM Output Splitter

Split long LLM responses into logical, collapsible sections with headings.

Paste a long ChatGPT or Claude response and split it into separately viewable, collapsible sections. Detects headings, blank lines and numbered points to find natural boundaries — runs entirely in your browser. It runs free in your browser on Gera Tools, with nothing uploaded.

Last updated Source: Gera Tools

How does it decide where to split?

It looks for Markdown headings (#, ##), bold-line pseudo-headings, numbered or bulleted top-level items, and runs of blank lines. The strongest available signal is used so a structured answer splits cleanly.

Split a long LLM answer into readable sections

Large language models love to produce long, dense answers — multi-part explanations, step lists, comparisons and caveats all in one block. This tool takes that wall of text and breaks it into logical, collapsible sections so you can scan the structure, jump to the part you need, and copy sections one at a time. Everything runs locally in your browser.

How it works

The splitter ranks the structural signals present in your text and uses the strongest one:

  1. Markdown headings (#, ##, ###) — each heading starts a new section.
  2. Bold-line pseudo-headings — a short line wrapped in **...** on its own.
  3. Top-level numbered or bulleted items (1., -, *) — each item becomes a section.
  4. Blank-line paragraph gaps — runs of empty lines separate paragraphs.

If none of those are present, it falls back to grouping sentences into roughly even chunks. Each section gets a heading (taken from the marker line, or generated from the first sentence) and a word count, so you can see at a glance how the answer is balanced.

When this tool is most useful

Researching a topic with Claude or ChatGPT. When you ask for a comprehensive overview, the model often returns a 1,000-word answer with eight sub-topics. The splitter lets you expand only the section relevant to your immediate question and copy just that part into your notes.

Debugging a long structured response. Models returning JSON, a multi-step plan, or a long numbered list are especially easy to navigate once split — you can jump to step 7 without scrolling past the first six.

Comparing two model responses. Split both into sections and scan the structure side by side to see where the answers diverge.

Extracting action items. After splitting, the word count per section quickly reveals where the model buried its recommendations under excessive caveats. You can then prompt it to expand just that section.

Split mode guide

ModeBest for
Auto (default)Mixed responses — the tool picks the strongest signal
Headings onlyLong structured answers where numbered items belong under their heading
ParagraphsUnstructured prose with no headings or lists
SentencesVery dense text with no structural markers at all

Code and JSON blocks

Fenced code blocks (surrounded by triple backticks) are kept intact inside a single section — the splitter never cuts mid-fence, so code samples, JSON objects, and command examples always appear whole. If you paste a response that is mostly JSON, the entire block stays together and the structural split happens around it.

Tips and notes

  • If the split is too fine, switch to Headings only so numbered list items stay grouped under their parent heading.
  • The word count per section is a quick way to spot a bloated section that the model padded out — a candidate for a follow-up “shorten section 3” prompt.
  • To copy a specific section as clean text for pasting elsewhere, expand it and use the section-level copy button rather than selecting the text manually.
  • Nothing you paste is sent to any server — the splitter runs entirely in your browser.