This paper investigates how to effectively incorporate domain knowledge into LLM-based code generation systems for quantum software development. The researchers evaluate various strategies including parameter-specialized fine-tuned models and general-purpose LLMs enhanced with retrieval-augmented generation and agent-based inference mechanisms. Using the Qiskit-HumanEval benchmark, they compare different approaches to quantum code generation with Qiskit frameworks. The study finds that modern general-purpose LLMs with advanced inference techniques consistently outperform specialized fine-tuned baselines, achieving approximately 47% pass@1 performance. These findings suggest that general-purpose models with retrieval and execution feedback mechanisms may be more suitable for evolving software ecosystems compared to domain-specific specialized models.