You are building a structured data extraction system using Claude. The system extracts information from…

Question

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.

Your extraction system uses tool_use with a JSON schema containing 12 fields and detailed descriptions, totaling approximately 2,500 tokens for the complete tool definition. Processing documents under 150K tokens yields 98% accuracy. For documents between 175-190K tokens, accuracy drops to 71%, with information from the final third consistently missed. The model's context window is 200K tokens.

What is the most likely cause?

Accepted Answer

D. Tool definitions consume input context tokens. Combined with system prompts and document content, the total approaches the context limit, degrading end-of-document processing.

Answer

A. The model distributes attention proportionally across input length, causing fields mentioned only once near the document's end to receive insufficient processing focus.

Answer

B. Very long documents exceed the model's effective attention span regardless of context limits, causing accuracy degradation for content farther from the prompt instructions.

Answer

C. Schemas exceeding 8-10 fields increase decision complexity during parameter generation, reducing extraction accuracy independent of document length.

Question 109