My RAG Chatbot Had 400 Documents But Gave Garbage Answers (The Document Quality Fix) 🔥

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Built perfect RAG system for client knowledge base. Indexed 400 documents. Beautiful vector search. Lightning-fast retrieval.

One problem: Completely useless answers.

"What's our refund policy?"

Agent: "I found 3 documents mentioning refunds."

That's not an answer. That's a search result.

Client needed actual answers from policy documents, not document lists.

THE IRONY:

RAG system using community template. Embedding, Qdrant vector store, retrieval logic - all brilliant.

But feeding it garbage document text. Scanned PDFs with broken parsing. Tables rendered as random characters. Multi-column layouts reading wrong direction.

Vector store full of corrupted text. Agent retrieving nonsense. Confidently wrong.

DISCOVERY MOMENT:

Checked what the RAG actually stored. Policy document saying "NET 30 PAYMENT TERMS" got indexed as "N E T 3 0 P A Y M E N T T E R M S" with random line breaks.

Agent couldn't match queries because stored text was destroyed during basic PDF extraction.

Perfect RAG. Broken input.

THE FIX:

Added document preprocessing before RAG ingestion.

Parse documents properly FIRST → Clean structured text → THEN feed to vector store.

Now extracts: Tables stay tables. Multi-column reads correctly. Headers separate from body text. Scans get OCR'd properly.

TRANSFORMATION:

Same question: "What's our refund policy?"

Before: "I found 3 documents mentioning refunds"

After: "Full refund within 30 days if unused. After 30 days, store credit only. Shipping not refundable. See Section 4.2 of Customer Policy."

Same RAG template. Just clean document input.

THE NUMBERS: