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DAIf you treat a complex PDF like plain text, your RAG will lie confidently.
Here's how I implemented the same production level solution 👇
1️⃣ Build a layout-aware ingestion pipeline.
Use OCR for scanned PDFs. Extract text with hierarchy, parse tables as structured data (not flattened text), and process charts/images separately.
2️⃣ Create modality-specific representations.
Text → semantic chunks.
Tables → JSON + summary for retrieval.
Charts → vision captioning + metadata.
3️⃣ Store in a vector DB with rich metadata.
Use hybrid retrieval (dense + keyword) and add a re-ranker for precision.
4️⃣ Construct a grounded prompt.
Inject retrieved context only. Enforce citation and “no external knowledge” policy.
5️⃣ Add evaluation + observability.
Track precision, hallucination rate, citation match, and latency breakdown. Continuously refine chunking and retrieval.
For extracting data from complex graphs and charts I'd suggest a hybrid approach of OCR extraction+Vision LLM.
This method will control the hallucination by providing accurate context to the LLM.
#ai #rag #llm #foryou #explorepae
@datamazing_girl
