Most real documents are not clean text. They are PDFs with tables, scanned pages, screenshots, charts, and diagrams. Multimodal RAG extends retrieval beyond plain text so the answer can come from a table cell, an image, or a figure — not just a paragraph.

Why it matters

The answer a user needs is often trapped in exactly the part naive RAG throws away. A specification sits in a table; a signature and author sit on a scanned title page; a trend lives in a chart. If your pipeline only ingests running text, those answers are invisible no matter how good your retrieval is.

The core techniques

• Structure-aware extraction — pull tables as tables and preserve rows and columns, rather than flattening them into an unreadable smear.
• Image and figure handling — generate a text description of each image or chart at ingestion time and embed that alongside the original.
• OCR for scans — turn scanned or image-only pages into searchable text (the fix for author-on-a-title-page failures).

Integrations do the heavy lifting here

Document extraction is deterministic, fiddly work — a textbook case for a skill that bundles code. Anthropic's document skills ship with scripts that read PDFs and extract fields; Claude runs the script rather than reasoning pixel by pixel. Use the same pattern: a `document-ingestion` skill carries extraction scripts, and the `SKILL.md` tells Claude which to run for which file type.

The pre-built document skills (PDF, DOCX, PPTX, XLSX) are available to install and read as reference — a strong starting point rather than building extraction from zero.