PDF to Text vs PDF to Markdown: Which Output Is Better for AI?
Compare plain text and Markdown output from PDF extraction. Learn when each format is better — for LLM context, RAG pipelines, data extraction, and document processing workflows.
When you extract content from a PDF, the output format matters — especially if you are feeding it into an AI model, building a RAG pipeline, or automating document processing. Should you extract plain text or Markdown?
This guide compares both formats so you can make an informed decision based on your use case.
The Core Difference
Plain text extraction strips all formatting and returns only the raw characters. Headings, bold text, tables, and lists are lost — you get a flat sequence of words.
Markdown extraction preserves document structure. Headings become ## or ###, bold text becomes **bold**, lists become bullet points, and tables remain readable. The result is a structured document that retains the original hierarchy.
| Aspect | Plain Text | Markdown |
|---|---|---|
| File size | Smallest | Slightly larger |
| Structure | None | Headings, lists, tables |
| Human readability | Moderate | High |
| LLM context efficiency | High (no markup tokens) | Good (structure helps) |
| RAG chunking accuracy | Lower (no section boundaries) | Higher (headings define chunks) |
When to Use Plain Text
Plain text is the simplest format. It is useful when:
1. Maximising LLM Context Tokens
Every token counts when you have limited context windows. Markdown adds structural tokens (#, **, |) that consume context without adding semantic value for many tasks. For a 10-page PDF, the markup overhead can be 5–15% of tokens.
2. Full-Text Search Indexing
Search indexes typically operate on raw text content. Markdown symbols like # and ** add noise that is not useful for keyword matching or vector embeddings. Plain text keeps the index clean.
3. Simple Downstream Processing
For tasks like keyword extraction, entity recognition, or sentiment analysis, plain text is sufficient and faster to process.
When to Use Markdown
Markdown shines when document structure matters to your application.
1. RAG Pipelines
Retrieval-Augmented Generation works better when chunks are semantically meaningful. A Markdown heading like ## Installation clearly signals where a new section starts, making chunking more accurate. Plain text might split the same content mid-paragraph.
2. Document Q&A Systems
When an LLM answers questions about a document, Markdown helps it understand context. "What does the pricing section say?" is easier to answer when the model can identify a ## Pricing heading versus guessing from flat text.
3. Human-Readable Output
If the extracted text will be displayed to users — in a dashboard, CMS, or review tool — Markdown preserves readability. Tables, lists, and code blocks remain recognisable.
4. Multi-Format Pipelines
Markdown can be converted to HTML, PDF, or other formats more faithfully than plain text. It is a good intermediate format for publishing workflows.
Real-World Example
Here is how a typical PDF section looks in both formats:
Original PDF heading: "Installation Guide" with sub-text: "Before you begin, ensure Python 3.9+ is installed."
Plain text:
Installation Guide Before you begin, ensure Python 3.9+ is installed.
Markdown:
## Installation Guide
Before you begin, ensure Python 3.9+ is installed.
The Markdown version clearly separates the heading from the body, which helps both humans and machines understand the document structure.
Choosing the Right Format
| Use Case | Better Format | Reason |
|---|---|---|
| LLM prompt context | Text | Fewer tokens = more room for content |
| RAG with chunking | Markdown | Headings define logical chunks |
| Full-text search | Text | Clean indexing, no markup noise |
| Display to users | Markdown | Readable tables, lists, headings |
| Multi-format publishing | Markdown | Convertible to HTML, PDF, DOCX |
| Simple data extraction | Text | Faster, smaller, uncluttered |
Using Both: The Hybrid Approach
Some extraction APIs support both output formats. You can store the Markdown version for display and RAG chunking, and generate a plain-text version on the fly for LLM context injection.
Document extraction APIs like gettxt.ai support both output formats, so you can choose per document or per use case without switching tools.
Conclusion
There is no universally "better" format — it depends on your pipeline:
- Use plain text when you need maximum token efficiency for LLM prompts, or when building full-text search indexes.
- Use Markdown when document structure matters for RAG chunking, user-facing displays, or multi-format publishing.
Choose a document extraction API that supports both formats, so you can decide per use case rather than committing to one output format upfront.