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.