PDF Parsing Is the Hidden Bottleneck in Your RAG Pipeline

When a RAG system returns hallucinatory or plain wrong answers, our first instinct is usually to blame the retriever or swap the LLM for a bigger one.

However, while stress-testing an end-to-end RAG pipeline on table-heavy Korean documents, I realized the real culprit was located one stage earlier: the PDF parser.

If your parser shreds a table into a stream of flat text, no amount of prompt engineering or high-dimensional embeddings can recover the lost structural context.

How Bad Parsing Destroys Retrieval

Context Pollution: When basic parsers inject broken encoding, missing whitespace, or garbage metadata into text chunks, embedding models end up vectorizing noise. Retrieval precision drops immediately.

PDF Parsing Is the Hidden Bottleneck in Your RAG Pipeline

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