arXiv:2604.09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty. We present a unified framework for ontology-constrained generation. First, we introduce a modular prompt-engineering architecture combining XML-style structure, expert disambiguation rules, chain-of-thought reasoning, metadata-aware decision logic, schema contracts, and a self-validation gate. It targets recurrent in-context fa
The increasing complexity of large language models and the demand for more reliable and controllable outputs are driving innovation in structured prediction and prompt engineering.
This development addresses key issues in AI deployment, making LLMs more reliable for critical applications requiring high accuracy, structural validity, and evidence grounding.
The ability to enforce ontological constraints and improve output quality through modular prompt engineering will accelerate the adoption of LLMs in structured data environments.
- · AI developers
- · Enterprises adopting AI
- · Structured data platforms
- · Semantic web technologies
- · Platforms with weak data governance
- · Manual data annotation services
- · Generative AI lacking control structures
Improved reliability and consistency of large language model outputs for specific tasks.
Accelerated integration of LLMs into critical business processes requiring high data accuracy and compliance.
The development of new industry standards and best practices for ontology-driven AI system design and validation.
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Read at arXiv cs.AI