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Business Requirements often fail to fully capture the complete set of system requirements they intend to describe, resulting in under-specification and measurable gaps between documented requirements and production-ready implementations. While experienced business analysts attempt to mitigate these gaps using domain expertise, the resulting artifacts often remain incomplete. This study uses Cross-Pollination, a domain-agnostic methodology for transforming incomplete natural language Business Requirements into comprehensive, production-grade functional user stories. To evaluate the completeness of LLM-generated user stories, the study systematically benchmarks them against realistic production artifacts. It further investigates the capability of Large Language Models (LLMs) to bridge the coverage gap between human-authored backlogs and production systems. It identifies the requirements that are overlooked during human-driven elicitation but are recovered through an LLM-assisted approach. The cross-pollination technique is a structured multi-phase methodology supported by a gap analysis taxonomy and a domain complexity calibration mechanism to establish a minimum baseline for production readiness. The methodology is applied across real-world domains, and a comparative evaluation of analyst-authored, LLM-generated, and production-derived user stories is conducted. Empirical results demonstrate that a substantial proportion of requirement gaps can be systematically identified and addressed through this approach. The findings indicate that Cross-Pollination provides a reproducible and scalable methodology for translating natural-language requirements into more complete and production-aligned user stories. The results demonstrate that while analysts produce precise requirements, LLM-generated user stories achieve higher recall across all evaluated domains and are effective in uncovering implicit and non-obvious requirements. These findings indicate that an integrated approach, with LLM-assisted elicitation functioning as a complementary mechanism to human expertise, produces more comprehensive and reliable requirement backlogs than either method independently.