LLMOps & RAG Evaluation

What Is LLMOps?

LLMOps — Large Language Model Operations — is the discipline of deploying, monitoring, evaluating, and continuously improving LLM-powered applications in production. It is to AI applications what DevOps is to software engineering: a set of practices, tools, and cultural norms that bridge the gap between building a model or application in development and running it reliably, cost-effectively, and safely at scale.

Traditional software monitoring tracks metrics like latency, uptime, and error rates. LLMOps adds an entirely new dimension: the quality of the model''s outputs. An LLM application can return a 200 OK response while simultaneously hallucinating a fact, misunderstanding a user''s intent, or leaking information it should not have. LLMOps is how you catch and fix those failures systematically.

The LLMOps Lifecycle

A mature LLMOps practice covers four stages. Development and experimentation involves prompt engineering, model selection, and pipeline design. Evaluation before deployment uses automated test suites and human review to establish quality baselines before any code ships to production. Production monitoring tracks real user interactions for quality regressions, latency spikes, cost anomalies, and safety issues in real time. Continuous improvement closes the loop by using production failures and user feedback to improve prompts, retrieval configurations, and fine-tuning datasets.

Essential LLMOps Tools in 2026

LangSmith (by LangChain) is the most complete observability platform for LLM applications. It provides end-to-end tracing of every chain and agent run, a dataset management system for building evaluation test suites, and an automated evaluation framework that scores outputs on correctness, faithfulness, and custom criteria. Langfuse is the leading open-source alternative — self-hostable, privacy-first, and deeply integrated with all major frameworks. Arize AI and Weights & Biases are popular in enterprise environments where teams need model performance monitoring alongside traditional ML metrics.

RAG Evaluation: The Hardest Problem in LLMOps

Evaluating a RAG system is particularly challenging because failure can occur at two stages: retrieval (did you find the right documents?) and generation (did the model use those documents correctly?). RAGAS is the de facto standard evaluation framework for RAG systems, providing automated metrics for context precision, context recall, faithfulness, and answer relevancy — all measurable without manual labeling using an LLM as an evaluator. DeepEval extends this with a broader suite of metrics including hallucination detection, toxicity, bias, and custom goal-based metrics.

Key Metrics Every LLM Application Should Track

Beyond standard latency and error rate monitoring, LLM applications should track: hallucination rate (what percentage of responses contain factually incorrect claims?), retrieval precision and recall (for RAG systems), token cost per query (to catch prompt bloat before your bill explodes), human evaluation agreement rate (how often do automated scores align with human judgment?), and user feedback signals (thumbs up/down, regeneration rate, session abandonment) as proxy measures of output quality.

Getting LLMOps right is what separates teams that successfully scale AI applications from those that get stuck firefighting production incidents. The resources in this collection will help you build the evaluation, monitoring, and continuous improvement infrastructure your LLM application needs — from running your first automated eval to building a fully instrumented production pipeline.