LLMOps services

We set up the practices and tooling that let a team change an LLM application with confidence: evaluation sets, tracing, versioned prompts and models, automated checks in CI, and monitoring in production.

What we build

Language model behavior is probabilistic and shifts when a prompt, a model version or a retrieval index changes. Without measurement, every change is a guess. LLMOps work gives you the measurements.

• Evaluation sets and scoring. A curated set of inputs with expected behavior, scored by exact checks, rubrics, human review or a model used as a judge, with the judge itself spot-checked by people.
• Tracing. Structured records of each request: the prompt as sent, retrieved passages, tool calls, model and parameters, latency, token counts and the output, so a bad answer can be reconstructed.
• Prompt and model versioning. Prompts stored as code or in a registry, with identifiers attached to every trace, and a documented way to promote or roll back a version.
• CI for LLM changes. Pull requests that touch prompts, retrieval settings or model choices run the evaluation set and report differences before merge.
• Production monitoring. Cost, latency, error and refusal rates, output format failures, and sampled review of real conversations.
• Guardrails and rollout. Input and output checks, rate limits, fallbacks to another model, and staged release of changes.

For the application itself, see NLP and LLM applications; for classical models, see MLOps.

How an engagement runs

1. Review the current system. Where prompts live, how changes are made, what is logged, and which incidents have happened.
2. Build the evaluation set. From real usage where permitted, plus hand-written hard cases and known past failures, with a scoring method for each type of check.
3. Add tracing. Instrument the application, decide what to capture and what to redact, and set retention.
4. Introduce versioning and CI. Prompts and configuration become reviewable artifacts, and evaluation runs on every relevant change.
5. Set up monitoring and alerts. Dashboards for quality proxies, cost and latency, with named owners for each alert.
6. Hand over. A written process for changing prompts and models, extending the evaluation set, and handling incidents.

Personal data in traces

Traces are useful because they contain the real prompt and answer, which also means they contain whatever personal data users typed. That makes your tracing system a data store with its own retention, access and deletion needs. We design redaction before storage where feasible, restrict who can read traces, set expiry, and make sure a deletion request can reach them. Read more in privacy in LLM applications, personal data in prompts and traces, handling deletion requests for LLM logs, self-hosted observability and data residency and data subject request automation. This is general information, not legal advice.

Typical stack

• Python or Node application code, with OpenTelemetry-style tracing and a trace store you host or a managed observability product, chosen to fit your data constraints.
• Evaluation harnesses written in Python, run locally and in GitHub Actions.
• Git for prompt and configuration versioning; a registry where non-engineers need to edit prompts.
• Dashboards in Grafana or your existing tools; budgets and alerts on token spend.
• Docker, Kubernetes and Terraform for the surrounding infrastructure; see DevOps.

Deliverables

• An evaluation set, scoring code and a baseline report.
• Instrumented application code and a trace schema with retention and redaction rules.
• CI workflows that run evaluations on relevant changes.
• Dashboards, alert rules and a short incident runbook.
• A change process document covering prompts, models and retrieval settings.

A note on tooling choices

The market for LLM observability and evaluation tools changes quickly, and we do not tie our recommendations to one product. We describe the capabilities you need, such as trace capture, dataset management, scoring and prompt history, and then map them to what you already use or to open-source and managed options that suit your data constraints. If you already have a tracing or evaluation tool, we use it. If you do not, a small amount of code and a dashboard often covers the first months, and we design the data formats so you can move to a dedicated platform later without losing history.

Whatever the tooling, the practice is the same: write down what good looks like, measure it on every change, keep a record of what was deployed when, and look at real traffic regularly.

Frequently asked questions

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