What is LLMOps (Large Language Model Operations)?

LLMOps is to AI what DevOps is to traditional software, the operational discipline that turns experimental models into reliable production systems. The framework encompasses five pillars. Prompt Management: versioning, A/B testing, and regression testing prompts across model updates (GPT-4o to GPT-5 may break prompts that worked perfectly). Observability: logging every LLM interaction with input tokens, output tokens, latency, and cost per request using tools like LangSmith, Helicone, or custom PostgreSQL logging. Hallucination Detection: automated evaluation pipelines that compare LLM outputs against ground-truth datasets, flagging responses that contain fabricated facts or contradict source material. Cost Optimization: implementing semantic caching (storing embedding-matched query-response pairs in Redis) to avoid redundant LLM calls, reducing API costs by 40-60% for repetitive queries. Rate Limit Management: building queue-based architectures that gracefully handle API rate limits during traffic spikes without dropping user requests. The most mature LLMOps implementations also include automated fine-tuning pipelines that collect production interaction data, filter for high-quality examples, and periodically retrain custom models to improve domain-specific performance.