Export ChatGPT Code Blocks to Markdown

What the fenced code block looks like in the export

When ChatGPT produces a code block, ChatCache exports it using standard Markdown fenced code block syntax. Here is what a Python block looks like in the raw .md file:

```python
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)
```

The language identifier comes directly from ChatGPT's own label for the block. ChatGPT tags every code block it generates - python, javascript, typescript, sql, bash, json, css, and so on. ChatCache reads that tag and places it after the opening triple-backtick fence. No language detection is performed by ChatCache - it preserves exactly what ChatGPT labeled.

Inline code is handled separately. When ChatGPT uses backtick-wrapped inline code - like mentioning a function name os.path.join() in a sentence - ChatCache exports it as backtick-wrapped inline code in the Markdown. Both forms render correctly in any editor that supports fenced code blocks - GitHub, GitLab, VS Code, Obsidian, Typora, and most documentation platforms.

How to export ChatGPT code to Markdown with ChatCache

1. 1Install ChatCache from the Chrome Web Store. Free, no account required.
2. 2Open the ChatGPT conversation containing the code blocks you want.
3. 3Click the ChatCache icon in your Chrome toolbar.
4. 4Select Markdown and click Download. The .md file is saved locally.

To export only the responses with code and skip unrelated messages, enter selection mode and check only the specific turns you want before downloading.

Where Markdown code blocks work

How the .md file renders across tools

The Markdown file ChatCache produces uses CommonMark-compatible fenced code block syntax, which is universally supported:

• GitHub - code blocks render with full syntax highlighting in READMEs, wiki pages, and pull request comments. A Python block renders with Python keyword highlighting; a SQL block renders with SQL-specific colors.
• VS Code - the Markdown preview panel renders fenced code blocks with the editor's active syntax theme. The raw .md file also opens cleanly in VS Code for editing.
• Obsidian - fenced code blocks render in Live Preview and Reading Mode with syntax highlighting. The file integrates with your vault's internal links and can be cross-referenced with other notes.
• Plain text editors (Notepad, TextEdit) - the raw fence syntax is visible. The code is readable, but the triple-backtick fences appear as literal characters rather than rendering visually.

Workflow: dropping exports into a /docs folder

A common developer workflow for ChatCache Markdown exports is to add AI-assisted content directly to a repository's documentation:

1. 1Use ChatGPT to work through an implementation - ask it to explain an algorithm, write a utility function, or draft an API reference section.
2. 2Open ChatCache, enter selection mode, and select only the assistant responses containing the content you want in the docs.
3. 3Export as Markdown. The file downloads locally - no server involved for Markdown export.
4. 4Move the .md file into your repository's /docs folder (or wherever your Markdown docs live).
5. 5Open in VS Code or your editor, add a front-matter header if needed, and edit to fit your documentation style.
6. 6Commit. The code blocks render correctly on GitHub, in Docusaurus, in MkDocs, or in any Markdown-based documentation system.

The key advantage is that the code blocks arrive already fenced and language-tagged - you do not need to manually add ```pythonwrappers around each snippet. They are correct in the exported file.

Developer use cases

Developers use Markdown export from ChatCache for several common workflows:

• Documentation - export AI-assisted implementation discussions as starting points for README sections or inline docs
• Knowledge base - save solutions to recurring problems in a personal or team Markdown wiki
• Code review prep - export a refactoring discussion with the proposed code as a reviewable Markdown document
• Obsidian notes - drop conversation exports into a vault to link them to related concept notes

Frequently asked questions