Evaluation & LLMOps

What it is

LLM applications fail in ways traditional tests miss: an answer can be well-formed and still wrong, and a prompt or model change can quietly degrade quality. Evaluation is the discipline of defining what good looks like and measuring it continuously — before release with test sets, and after release with live monitoring.

LLMOps extends MLOps to generative systems: versioning prompts and models, tracing every call, tracking cost and latency, routing between models, collecting user feedback, and rolling changes out safely. It is what lets an AI feature keep improving instead of drifting.

How it works

1. Define quality

   Agree on criteria per use case — correctness, faithfulness, tone, safety, format.

2. Build eval sets

   Collect representative and adversarial examples, with reference answers where possible.

3. Score

   Combine code checks, LLM-as-judge rubrics and human review.

4. Gate releases

   Run evaluations in CI for every prompt, model or retrieval change.

5. Trace in production

   Log inputs, outputs, tool calls, latency, tokens and cost for every request.

6. Close the loop

   Feed failures and user feedback back into the evaluation set.

Key concepts

Offline vs. online evaluation

Test sets before release; sampled scoring, feedback and A/B tests after.

LLM-as-judge

Using a model with a clear rubric to grade outputs at scale — calibrated against human labels.

RAG metrics

Context recall, precision, faithfulness and answer relevance.

Tracing

End-to-end records of each request, including nested retrieval and tool steps.

Model gateway

A single layer for routing, fallbacks, rate limits, caching and spend control across providers.

Drift

Quality changes caused by new data, user behavior or silent model updates.

Guardrail metrics

Rates of refusals, policy violations, PII detections and escalations.

Tools & platforms

Skills employers look for

• Designing task-specific evaluation criteria and datasets.
• Building and calibrating LLM-as-judge graders.
• Integrating evaluations into CI/CD as release gates.
• Instrumenting applications with tracing and cost and latency dashboards.
• Running A/B tests and safe rollouts of prompt and model changes.
• Turning production failures into regression tests.

Enterprise use cases

Common pitfalls

• Launching on vibes — a handful of manual tries is not an evaluation.
• Uncalibrated judges — LLM graders need spot-checks against human labels.
• No production tracing — you cannot fix what you cannot see.
• Static test sets — evaluation must grow with real-world failures.