Gera Tools

Section Extractor by Heading

Extract a specific named section from a long LLM markdown response.

Given a heading name, this tool extracts the matching section and all of its subsections from a long LLM markdown response, so you can copy out only the part you need from a verbose answer without manual scrolling. It runs free in your browser on Gera Tools, with nothing uploaded.

Last updated Source: Gera Tools

How does it know where a section ends?

A section runs from its heading until the next heading of the same or higher level. So extracting an H2 also pulls in any H3 and H4 subsections nested under it, but stops at the next H2 or H1.

Why extract one section

LLMs love to produce long, multi-part documents: a single answer might contain a summary, an analysis, a risk list, and a set of recommendations. Often you only need one of those parts — to paste into a ticket, feed into another prompt, or review on its own. Scrolling and hand-selecting the right block is error-prone, especially when subsections are nested several levels deep.

This tool finds the heading you name and pulls out exactly that section and everything under it.

How it works

The extractor scans the text for ATX markdown headings (lines beginning with # through ######) and builds a list of every heading and its level. When you choose a heading, it captures everything from that heading down to the next heading of the same or higher level. Because a higher-importance heading has fewer # characters, an H2 section automatically includes any H3 and H4 subsections beneath it but stops cleanly at the following H2.

If you type a heading name instead of selecting one, it matches case-insensitively and falls back to a partial match, so you do not have to reproduce the exact wording.

Practical workflows

Routing specific sections to specific people. When a model produces a full report with a Summary, Technical Details, and Next Steps, you can extract each section independently and forward the right piece to the right person — the executive summary to leadership, the technical section to engineering — without sharing the full document or manually cutting and pasting.

Feeding one section into a follow-up prompt. Large context windows help, but precise follow-up prompts work better when you give the model exactly the section you are discussing rather than the whole document. Extracting just the “Recommendations” section before asking “rewrite this in bullet points” gives tighter, more focused results.

Building a pipeline from a long structured response. When using an LLM to produce a multi-section analysis (SWOT, PRD, risk register), extract each named section programmatically and store them in separate fields rather than parsing unstructured text downstream.

Tips

  • If the dropdown is empty, your response is not using # markdown headings — ask the model to “use markdown headings” and regenerate, or paste the version that has them.
  • Extracting an H1 typically grabs the whole document below it; pick a more specific H2 or H3 to narrow the result.
  • The extracted block keeps its own heading line, so you can paste it straight into another markdown document and it stays well-formed.
  • Chain this with the TOC Generator to first see the document’s structure, then pull out just the section you care about.
  • Partial matching means you can type just “risk” to match a heading called “Risk Assessment” — useful when you do not remember the exact heading wording.