LLMOps

Why does LLMOps matter?

Enterprises adopting LLMs for production applications need to manage compute costs, evaluate output quality, and control what a model can access, work that goes well beyond getting a prototype running.

Regulated industries face a particular gap right now: SR 26-2, the Federal Reserve's April 2026 model risk management guidance, explicitly excludes generative and agentic AI from its formal scope, but the guidance is clear that institutions still need to apply their existing risk management principles to these systems. LLMOps is where that parallel governance actually gets built, since there's no equivalent formal rulebook yet to point to.

In life sciences, LLMs used to summarize clinical literature or draft regulatory documents still need the same audit trail and reproducibility expectations that apply to any other tool touching a regulated submission, which means prompt versions, model versions, and outputs all need to be tracked the way a traditional model's training run would be.

(Stale reference removed: the live page opens by citing "the launch of ChatGPT, Bard and Bing AI" as current context. Bard has since become Gemini, so this framing is dated and cut entirely rather than updated in place.)

How does LLMOps work?

• Data access and preparation: curating and governing the text-based data used for tuning, since data quality directly drives prompt and fine-tuning results.
• Training and fine-tuning: adapting a pretrained model through model tuning using one or more of three approaches: prompt engineering, fine-tuning, or retrieval-augmented generation (RAG).
• Evaluation: scoring outputs on accuracy, fluency, relevance, coherence, and bias, since traditional evaluation metrics like precision and recall don't fully capture language quality.
• Monitoring: tracking prompts, error rates, and toxicity alongside operational metrics like token usage, latency, and request volume.

How is LLMOps different from MLOps?

What are the components of LLMOps?

Governance and controls, data access and engineering, model customization and tuning, model evaluation, review and approvals, model deployment, tracking and monitoring, and FinOps (tracking the cost of running LLMs in production).

Best practices for implementing LLMOps

1. Manage data deliberately. Version data changes and enforce access controls, such as role-based access, on the datasets used for tuning.
2. Start from the right base model. Choose a suitable pretrained model, and use few-shot learning where it gets to a usable result faster than full fine-tuning.
3. Choose a deployment strategy on purpose. Base the approach on budget, security, and infrastructure requirements rather than defaulting to whatever's fastest to stand up.
4. Monitor continuously. Track model and pipeline lineage and versions, with alerts for drift and unusual usage patterns.

Industry use cases

Financial services

Financial institutions increasingly use LLMs to draft or summarize documents, like credit memos, that eventually feed a regulated decision. Because SR 26-2 excludes generative and agentic AI from its formal scope, institutions are building their own governance for this work, including prompt versioning and output logging, so a document's drafting process can be reconstructed months later.

Life sciences

A literature summary an LLM produces for an investigator's brochure needs to be traceable back to the exact prompt and model version that generated it, the same way a traditional analysis needs its code and environment on record, in case a regulator asks how a specific claim in the document was derived.

Public sector

Government agencies exploring LLMs for tasks like processing public records requests or drafting constituent communications typically need to run them in secure, sometimes air-gapped environments, and are doing so without sector-specific regulatory guidance to define what that governance should look like yet.

FAQ

What is an LLMOps platform?

An LLMOps platform provides the infrastructure and tooling to develop, deploy, and manage LLMs in one collaborative environment, automating operational and monitoring work that would otherwise be done by hand across separate tools.

Is LLMOps part of MLOps?

Yes. LLMOps sits under the broader MLOps umbrella, with additional practices specific to the compute requirements, tuning methods, and evaluation approaches that large language models need.

What does LLMOps stand for?

LLMOps stands for large language model operations, the operational practices for managing LLMs from data preparation through production monitoring.

What's the difference between fine-tuning and RAG in LLMOps?

Fine-tuning updates a model's weights using a curated dataset, which changes the model itself. Retrieval-augmented generation (RAG) leaves the model unchanged and instead retrieves relevant information at query time to include in the prompt. Teams often choose RAG when the underlying data changes frequently, since it avoids retraining every time the data does.