Abstract: Large language models (LLM) are increasingly being incorporated into chip and FPGA development workflows. However, existing studies have mainly focused on RTL generation, assertion generation, or localized debugging, while systematic case studies covering end-to-end engineering workflows remain limited. Using SegTracer, a data-path latency probing tool for multi-FPGA prototyping systems, as a case study, this paper investigates the role of a human-led, LLM-assisted, verification-driven development paradigm in practical IP implementation. During the design and deployment of SegTracer, LLMs were used for requirements refinement, solution comparison, design document consolidation, SpinalHDL implementation, generation of register-access and verification scripts, and troubleshooting during localized debugging; human developers were responsible for key mechanism definition, literature verification, clock-domain-crossing and handshake-semantics handling, waveform analysis, and experimental interpretation. Technically, SegTracer employs independent probe packets together with a packet generation and monitoring module and a unified timestamp module. Through stack-based recording on the forward path, in-place subtraction on the return path, and centralized endpoint statistics, it achieves segmented latency measurement and distribution analysis without relying on global clock synchronization. Simulation and FPGA board-level experiments show that the tool can support latency decomposition and tail-bottleneck localization in both QSFP direct-link and PCIe hardware-software collaborative scenarios. The case study demonstrates that large language models can significantly improve design iteration, script development, and joint debugging and verification efficiency, while human leadership remains essential in key aspects such as domain-specific semantic understanding, clock-domain-crossing handling, timing correctness, and result interpretation.