Invariant-oriented methodology for the experimental verification of an LLM-based data structuring pipeline

When large language models are used for structuring textual data, it is insufficient to assess only whether a valid JSON or XML representation has been obtained. To substantiate the reliability of the software pipeline, syntactic correctness, compliance with formal constraints, type compatibility, semantic quality, and output reproducibility should be evaluated separately.

Goal – develop a methodology for the comprehensive experimental verification of an LLM-based data structuring pipeline that enables consistent assessment of its key properties and determination of the boundaries of its practical applicability.

An invariant-oriented experimental evaluation framework is proposed. It includes a main cross-provider experiment on a balanced Text-to-JSON sample, analysis of the internal quality structure of structured output, reproducibility testing, and additional experiments involving function calling, ontology-constrained structuring, and complex schema processing. The relationship between individual experiments and the evaluated pipeline properties is defined, and a principle for their joint interpretation is proposed.

The proposed methodology makes it possible to build a substantiated reliability profile of an LLM pipeline and use it to assess the system’s readiness for integration into applied information systems with specified requirements for formal correctness, extraction quality, and reproducibility.