LLMOps is the practice of running AI features reliably in production: tracing every model call, measuring quality and cost, catching failures, and improving prompts and context safely over time. Traditional monitoring tells you the server is up. It won't tell you the chatbot started confidently giving wrong refund information on Tuesday.
What to capture on every AI call
- Inputs and outputs, with sensitive data redacted.
- Model, version and parameters.
- Tokens and cost.
- Latency, including time to first token for streaming.
- Retrieved context: which documents were used.
- Tool calls made by agents, and their results.
- User feedback: thumbs up or down, edits, escalations to humans.
OpenTelemetry now has conventions for generative AI telemetry, so this data can flow into the same observability stack as the rest of your app.
Measuring quality, not just uptime
- An evaluation set: real questions with known good answers, run on every change to prompts, models or retrieval.
- Automated checks: did the answer follow the format? Cite a source? Stay on topic?
- Human review of samples: read a few dozen real conversations every week.
- Track outcomes: tickets resolved, leads qualified, edits needed.
Alerts worth setting
- Cost per day or per feature spiking.
- Error or timeout rates from a provider.
- Sudden drops in user ratings.
- Unusual tool-call patterns from agents, which can indicate prompt injection.
Safe changes
Treat prompts and context like code: version them, test them against the evaluation set, roll them out gradually, and keep the ability to roll back instantly.
AI features you can trust
At NextGen Fusion, our AI development company in Lucknow, AI features ship with tracing, cost tracking and an evaluation set from day one, so clients can see what their AI is doing and how much it costs.
