How do I convert a PDF to Markdown for free?

Upload your PDF to mdisbetter.com, click Convert, and get clean structured Markdown in seconds. No signup, no installation — it works directly in your browser.

Why is Markdown better than PDF for AI?

Markdown reduces token usage by up to 95% compared to PDF when feeding documents to AI models like ChatGPT or Claude. PDF contains layout metadata, fonts, and binary data that waste tokens. Markdown preserves only the content structure that AI actually needs.

What file types can MDisBetter convert to Markdown?

MDisBetter converts PDF, Word (.docx), plain text, YouTube videos (transcript extraction), audio files (MP3, WAV, M4A, OGG, FLAC, WEBM), and any web page URL to clean Markdown.

Is MDisBetter free to use?

Yes, MDisBetter is completely free. You get 10 conversions per day with no signup required. All tools work directly in your browser.

How do I extract a YouTube transcript as Markdown?

Paste the YouTube video URL into the YouTube to Markdown tool on mdisbetter.com and click Convert. The tool extracts the transcript and structures it as clean, formatted Markdown with headings and timestamps.

Why is BeautifulSoup-scraped HTML bad for RAG?

Because the cleanup is per-site and brittle. You strip <script> , <style> , and a few common boilerplate selectors — and miss the next site's nav class, the comment thread on a third site, and the cookie banner on a fourth. Every chunk gets a different mix of boilerplate, polluting your embeddings.

How should I chunk web Markdown for retrieval?

Header-aware first, then recursive character splitter as a safety net. Target 600-1000 tokens per chunk, 50-150 overlap. Keep the source URL and heading path as chunk metadata — it makes retrieval debugging tractable and powers source citations in your RAG output.

Should I crawl recursively or convert one URL at a time?

Depends on the site. For documentation sites with clear navigation, crawl recursively from the index page. For news/blog content, convert specific URLs as you bookmark them. Recursive crawling without rate-limiting will get you blocked; respect robots.txt and add delays.

Does the converter handle JavaScript-rendered pages?

Yes — the converter runs a real browser engine before extracting, so React, Vue, Svelte, and other client-rendered pages produce Markdown the same way static pages do. This eliminates a major class of "missing content" failures common with raw requests.get() .

How do I attribute sources in my RAG responses?

Store the source URL as chunk metadata at ingestion time, then include it in your synthesis prompt: "When citing, include the source URL from each chunk's metadata." Most RAG frameworks expose chunk metadata to the LLM directly during synthesis.

URL to Markdown for RAG — Scrape, Convert, Embed

The web-scraping-into-RAG failure pattern

The naive pipeline is: requests.get(url), BeautifulSoup, strip a few tags, chunk by character count, embed. This works for the first 10 sites you try and falls apart on the 11th, which uses a different DOM convention. The chunks end up stuffed with "Skip to main content", "Subscribe to our newsletter", "© 2026 Some Company", and section headers from the unrelated sidebar. Every embedding gets pulled toward boilerplate.

Convert each URL to Markdown first — through MDisBetter's web tool for one-offs, or through a self-rolled OSS pipeline (Trafilatura, html2text, Readability.py) for automation. Either way, you skip the per-site DOM-wrangling and end up with clean prose plus real headings.

Two paths: web tool or OSS

One-off ingestion (a handful of URLs at a time): paste each URL into /convert/url-to-markdown, click Convert, save the .md file, run it through your chunker. We don't currently expose a programmatic API — for batch automation you'll want to roll your own with the OSS tools below.

Recommended pipeline

URL list → extract main content (Trafilatura is the best-in-class OSS extractor) → convert to Markdown (html2text or markdownify) → chunk on H2/H3 headings (header-aware splitter) → embed → store in vector DB with the source URL and heading path as metadata. For PDF sources, see PDF to Markdown for RAG.

Tool	Cost	Unit
Text to MD, EPUB to MD, MD to PDF, MD Cleaner, Merger, Chunker, Token Counter, Context Builder	Free	—
Word to MD	0.5 credit	per page
Excel to MD	0.5 credit	per conversion
Single URL Scrape	0.5 credit	per call
Site Crawl	1 credit	per page
Translate	1 credit	per 10 000 chars (min 1, free re-translation on cache hit)
Prompt Optimizer	1 credit	per call
System Prompt Generator	1 credit	per call
Audio to MD	2 credits	per minute
Video to MD	2 credits	per minute
YouTube to MD	2 credits	per minute
Image OCR	4 credits	per image (0 on cache hit)
PDF to MD	4 credits	per page
PPTX to MD	4 credits	per slide

URL to Markdown for RAG — Web Scraping to Knowledge Base

The web-scraping-into-RAG failure pattern

Two paths: web tool or OSS

Recommended pipeline

Code example

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