LLM Deployment Is Just the Beginning
When teams first get an LLM working in a demo, it feels like a solved problem. Then comes the production checklist: latency requirements, cost monitoring, prompt drift, hallucination rates, context window management, and the endless cycle of model updates. LLMOps is the discipline that makes LLM applications sustainable beyond the proof-of-concept stage.
Observability Is Non-Negotiable
The first thing you need in production is visibility into what your LLM is actually doing. Log every request: input prompt, output tokens, latency, cost, and—critically—the user satisfaction signal if you can capture it. Tools like Phoenix (by Arize), LangSmith, and Braintrust give you tracing, evaluation, and drift detection out of the box.
The most useful metrics are task-specific. For a customer support chatbot, track resolution rate and escalation rate. For a code generation tool, track acceptance rate of suggestions. Generic benchmarks tell you about the model; these metrics tell you about your application.
Prompt Versioning and A/B Testing
Prompt changes are code changes. Treat them with the same discipline: version control, code review, testing, and staged rollout. A prompt that worked in March may perform worse in May as the model's behavior shifts across updates, or as user behavior changes.
A/B testing prompts against real traffic—measuring task completion rate, not just token overlap—is the most reliable way to improve performance over time. Small changes in prompt wording can have outsized effects on output quality.
Cost Management at Scale
Token costs compound fast. At millions of requests per day, even a 10% reduction in token usage translates to significant savings. Techniques like prompt compression, semantic caching (storing embeddings of common queries and retrieving cached responses), and routing simple queries to smaller models are standard practice in 2026.
Context window management deserves more attention than it gets. Including irrelevant context not only wastes tokens but often degrades output quality. Teach your engineering team that less context is usually better.
The Model Update Problem
When you update to a new model version—or switch providers entirely—your application behavior changes, often in subtle ways that are hard to catch in testing. Automated regression suites that run against your evaluation dataset before every deployment are the practical answer. The dataset should be curated from real production failures, not synthetic test cases.