What is LLMOps?
Turkish: LLMOps (LLM Operasyonları)
LLMOps is the practice of testing, monitoring, versioning, and safely operating applications built on large language models.
What is LLMOps?
LLMOps defines how applications that use LLMs are operated after development. In addition to classic software monitoring, teams track prompt versions, model changes, source quality, cost, latency, and answer reliability.
In an LLM application, the same code can behave very differently with another model version or system prompt. LLMOps therefore versions code, prompts, evaluation datasets, and model configuration together.
What It Covers
- Evaluation: Quality checks with golden answers, failure cases, and security tests
- Observability: Token usage, latency, cost, error rate, and user feedback
- Versioning: Tracking prompts, models, fine-tuning, and RAG sources
- Security: Controls for data leakage, prompt injection, and unsafe output
Business Use
LLMOps is needed for customer support bots, internal knowledge assistants, document summarization, quote generation, and agent-based automation. A model that looks good in a prototype can still fail under real user traffic because of cost, quality, or security issues.
MLOps focuses on the lifecycle of machine learning models; LLMOps also covers the variability of natural language output and external tool use.
Related Terms
Fine-tuning retrains a pre-trained model on selected examples so it behaves more consistently for a task, tone, or domain.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
MLOpsMLOps manages machine learning data, training, deployment, monitoring, and retraining with production engineering discipline.