Tips, tutorials, and insights on PDF to Excel conversion and data extraction.
Structured outputs guarantee the shape of your JSON, never the truth of the values. Here is how to design extraction schemas with nullable fields and evidence spans so wrong answers surface.
LlamaParse preserves table geometry but loses meaning. Claude API reads meaning but drifts on layout. Here is why the accurate stack uses both plus a reconciliation check.
Model deprecations, prompt drift, new vendor layouts, and on-call for failed batches are the costs most build-versus-buy math leaves out. Here is how to price them.
Hallucinated numbers are not random. They cluster around specific document conditions, and knowing which ones lets you contain the risk instead of waiting for a better model.
Real per-page token math for LLM document extraction, including the reasoning tokens, retries, and validation passes that push the loaded cost to roughly triple the quoted rate.
MCP standardizes how an extraction agent reaches files, parsers, and destination systems. It fixes the plumbing and the orchestration, not whether the extracted numbers are right.
Line-item prompts break on totals, discount rows, and page continuations. The fix is arithmetic self-checks and explicit null rules, not longer instructions.
Most teams build RAG when they actually need deterministic field extraction. Here is the question-versus-record test that tells you which problem you have before you build.
Hallucinated amounts are not scattered at random. They cluster around bad scans and running balance columns, and a balance continuity check makes them loud.
LLM extraction fails silently on financial documents, producing plausible wrong numbers. Here is a four-tier validation stack that catches those errors before they hit your ledger.
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