Prepare Documents for RAG with Markdown

For retrieval-augmented generation (RAG), document conversion is the normalization step between source files and indexing. AnydocAI converts mixed document formats into Markdown so the extracted text can be reviewed before it enters a retrieval pipeline.

A reliable preparation workflow

1. Classify the source. Separate text documents from scanned PDFs and encrypted files.
2. Convert to Markdown. Use the local converter for supported text-based files.
3. Review structure. Check headings, tables, lists, page order, and missing text.
4. Attach source metadata. Preserve filename, document version, access policy, and stable source identifier outside the Markdown body.
5. Chunk for the retrieval system. Split only after the document structure and metadata are known.
6. Evaluate retrieval. Test representative questions against the indexed output and inspect the cited source passages.

Why review before chunking

Chunking cannot recover content that was lost or placed in the wrong reading order during extraction. Reviewing Markdown first makes parser problems visible before they become retrieval problems.

Recommended metadata

At minimum, retain a stable document ID, source filename, version or updated date, access scope, and section heading for each indexed chunk. Keep confidential source files and private generated results outside public URLs.

What AnydocAI does in this workflow

AnydocAI handles document-to-Markdown conversion. Your retrieval stack remains responsible for chunking strategy, embeddings, vector or search storage, access control, evaluation, and answer generation.

Check supported formats before preparing a mixed collection.