Production Standstill

This year, several data leaders began thinking about Large Language Model Operations (LLMOps) at a pivotal moment: when promising AI experimentation was ready to be transformed into business value. That's when the factory floor stopped. Quizzical practitioners and befuddled leaders debated questions they had foreseen but not answered — questions that could be summed up as:

How are we going to operationalize AI?

They would have benefited from an understanding of LLMOps during their experimentation phase — not because best practices and formal operational processes are most valuable during iterative exploration (some argue they are least valuable then) — but because LLMOps is the answer to many of the conundrums they faced:

How do I deploy AI apps widely?

Who does what?

How do I scale AI apps?

How do I monitor and control compute costs?

How do I maintain and improve model performance over time?

How do I reduce hallucinations and data privacy risks?

How do I improve response accuracy to drive business value?

The answer to all these questions? LLMOps.

Nuts and Bolts

Naturally formal operations processes fueled by best practices improve effectiveness, reliability, scalability, accountability and repeatability. They also reduce risk and improve efficiency. However, LLMOps' predecessors, MLOps and DevOps, offer no guidance on training and maintaining LLMs, optimizing model performance and accuracy, or hawkwatching a voracious kettle of GPUs.

New frameworks and workflows are needed to guide model building, training, and deployment. Without them, it will remain difficult to test model accuracy, ground hallucinations, and recalibrate drift. Even improvisational activities like exploratory data analysis benefit from LLMOps, as these processes preserve the history and impact of experimentation on model output.

For data leaders accountable for delivering value through AI, LLMOps is fundamental for monitoring and controlling compute costs and scaling enterprise AI applications. As AI scales, LLMOps automates pipelines and streamlines model development, testing, and deployment with continuous integration and delivery (CI/CD).

But the greatest advantage of LLMOps isn't technical — it's collaborative. Natural Language Processing (NLP) has lowered the technical barrier for non-technical users to extract high-value insights. With their deep subject-matter expertise, business users are becoming key contributors to AI workflows. The tool most essential for this collaboration? The simplest machine on the factory floor: the suggestion box.

The Suggestion Box

The most important tool in the LLMOps factory is the feedback loop — between prompter and responder, user and engineer, AI and AI. It's the secret to AI accuracy and effectiveness and the crux of LLMOps.

On the factory floor, users improve responses through better prompt engineering. This isn't technical engineering; it's simply about improving the plain-language instructions users submit to the AI. They give thumbs-up or thumbs-down responses and provide comments on model failure.

Behind the scenes, data analysts and data engineers, whether centralized in a Data and Analytics COE or distributed in a data mesh architecture, use feedback to improve the quantity or quality of data to increase response accuracy, or fine-tune the model to drive specific, desired behaviors.

The Beginning of the Assembly Line

Where should organizations start with LLMOps? A common construction pattern looks like this:

Model Selection: Organizations often target productivity and efficiency gains as drivers for AI deployment. They typically begin with a foundational model to democratize AI use across pockets of the enterprise. Model selection involves weighing quality, accuracy, functionality, speed, latency, and cost.

Model Adoption and Safe Usage: Foundational model deployment enables retrieval-augmented generation (RAG) to improve responses with internal data. Clear guidelines and guardrails must define which data users can expose to AI models, under what circumstances, and for which use cases.

Model Accuracy: This is the primary objective of LLMOps. Even minimal training in prompt engineering can significantly improve outputs and adoption. The suggestion box further boosts accuracy through iterative feedback.

Scalability: LLMOps determines how AI tools are deployed — whether by sharing prompts and tools across teams or by leveraging agentic frameworks where multiple specialized models collaborate on complex tasks.

Model Monitoring and Control: Use LLMOps to continuously monitor and improve model performance — accuracy, latency, safety, and compute costs.

Without LLMOps, you might find yourself operating in a sLLOMp. And while I'm not sure what that is, it certainly doesn't sound good.