The transition of Large Language Models (LLMs) from experimental notebooks to production-grade applications requires more than just a well-crafted prompt. As enterprises integrate Generative AI into their core workflows, the need for stability, scalability, and reproducibility becomes paramount. This is where LLMOps—the intersection of DevOps, Data Engineering, and Machine Learning—enters the frame. Building a CI/CD pipeline for LLM-based applications on Google Cloud Platform (GCP) presents unique challenges. Unlike traditional software, LLM outputs are non-deterministic, making testing complex. Unlike traditional ML, the "model" is often a managed service (like Gemini) or a fine-tuned version of an open-source giant, shifting the focus from training to orchestration, prompt management, and RAG (Retrieval-Augmented Generation) infrastructure.…