DataOps Meets LLMOps: Automating Cloud-Based AI Workflows from Data Ingestion to Prompt Optimization

The fast advancement of Large Language Models (LLMs) created difficulties for organizations to maintain end-to-end AI workflows which start with raw data processing and end with prompt optimization. The framework unites DataOps with LLMOps principles to handle automated cloud-based AI pipelines. The proposed system combines DataOps data sourcing with validation and transformation practices and aligns them with LLMOps model fine-tuning capabilities and prompt engineering and performance monitoring functions to achieve unified AI lifecycle component orchestration. Through combination of containerized microservices and continuous integration/continuous deployment mechanisms and cloudnative tools (specifically Kubernetes, Terraform as well as MLflow) the proposed system delivers adaptable and scalable AI workflows with reproducible execution. Experimental tests confirm notable reductions in latency together with improved cycle time for data-to-insight processing and faster prompt processing of various NLP applications. The study provides innovative operational methods to develop intelligent systems driven by data alongside prompt awareness for future AI automation systems.