Author(s): Katakamvivek

Originally published on Towards AI.

What 80 documents taught me about cost, latency, hallucination, and picking the right tool for document processing.

Somewhere along the way, LLMs became the default answer to every document problem. Need to extract data from an unstructured document and turn it into structured JSON? Throw it at an LLM. Need to classify what type of document just landed in your pipeline? LLM. Need to pull text out of a scanned image? LLM again.

The article compares Azure AI Content Understanding (Read OCR) with multimodal LLM extraction using a benchmark across 80 documents, focusing on cost, latency, hallucination risk, and token utilization. It explains how Read OCR provides deterministic, per-word confidence grounded in the source document but requires additional post-processing to map text into a target schema, while multimodal LLMs can output structured JSON directly and handle reasoning—but may hallucinate and typically only provide confidence at an overall level. The author walks through example outputs, highlights why per-field/word confidence matters for production reliability, and proposes a decision framework: use multimodal LLMs when reasoning is primary, use Read OCR when accurate extraction is primary, and prefer an architecture that combines both (OCR first for extraction, then an LLM for structuring/validation). Finally, it summarizes key benchmark findings on performance and pricing and concludes with practical pipeline guidance such as confidence-based human review, rule-based validation for critical fields, caching OCR results, and benchmarking token utilization on your own data.

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Published via Towards AI