What It Is
LLMOps extends MLOps with concerns specific to language models: prompt versioning, evaluation of free-form outputs, retrieval management, guardrails, and token cost tracking. It treats prompts, retrieval settings, and model versions as assets to be tested and tracked, just like code.
Key Points
- Automated evaluation: test suites combined with human feedback loops.
- Tracing: follow every request through retrieval and generation.
- Safe rollouts: canary releases and caching.
- Risk management: latency, reliability, and prompt injection.
Why It Matters
LLM outputs are open-ended, so small changes in prompts, models, or data can shift results. Good LLMOps gives teams confidence to change models or prompts without breaking behavior that users already depend on.
How ClearLeaff Applies It
We deliver the full lifecycle, including hallucination monitoring and token cost optimization, using Kubeflow, MLflow, and vLLM to move clients from demos to dependable platforms.