Practice · Fast-moving · Intermediate

LLMOps (2)

Operating language-model applications: managing prompts, evaluations, cost, latency, safety and provider changes.

What LLMOps (2) is

LLMOps differs from MLOps because the model is usually someone else's and the output is open-ended text. The artefacts you version are prompts, tools, retrieval indexes and evaluation sets.

How it works

Teams keep prompt versions in source control, run automated evaluation suites on every change, trace every request with token and cost accounting, cache aggressively, and maintain fallback providers.

Why it matters

Without evaluation and tracing, nobody can tell whether a prompt change or a silent provider update improved or degraded the product.

Common uses

→Prompt regression testing
→Cost and token monitoring
→Provider failover
→Safety filter tuning

Strengths

✓Catches regressions before users
✓Controls spend

Watch for

✓Evaluation of open-ended output is genuinely hard
✓Fast-moving tooling

Continue exploring

LLM Evaluation AI Orchestration Prompt Engineering AI Observability