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What is LLMOps, and how is it different from MLOps?

LLMOps uses specialized tools and best practices to manage Large Language Models. We explain what you need to know and the difference between MLOps and LLMOps.

Apr 15, 2024 • 7 Minute Read

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  • IT Ops
  • AI & Machine Learning

Have you heard of Large Language Model Operations (LLMOps)? LLMOps introduces tools and best practices that help you manage the lifecycle of LLMs and LLM-powered applications. 

In this post, I’ll explain how LLMOps differs from MLOps and how LLMs will impact Generative AI adoption.

How do organizations use Large Language Models (LLMs)?

Training foundation LLMs, such as GPT, Claude, Titan, and LLaMa, can be a significant financial undertaking. Most organizations lack the hefty budget, advanced infrastructure, and seasoned machine learning expertise to train foundation models and make them viable for building Generative AI-powered systems. 

Rather than training a foundation model, many businesses explore more economical alternatives to incorporate LLMs into their operations. However, each choice still requires a well-defined process and the right tools to facilitate development, deployment, and maintenance. 

Prompt engineering

Prompt engineering involves the skillful creation of text inputs, known as prompts, to steer an LLM towards producing the intended output. Techniques such as few-shot and chain-of-thought (CoT) prompting enhance the model's accuracy and response quality. 

This method is straightforward and allows businesses to interact with LLMs through API calls or user-friendly platforms like the ChatGPT web interface.


This method is similar to transfer learning. Fine-tuning means adapting a pre-trained LLM for a specific use case by training it on domain-specific data. 

Fine-tuning enhances the model's output and minimizes hallucinations (answers that sound logically correct but are inaccurate). Although the initial costs of fine-tuning might be pricier than prompt engineering, the advantages become evident during inference. 

By fine-tuning a model with an organization's proprietary data, the resulting prompts during inference are more concise and require fewer tokens. This improves model efficiency, speeds up API responses, and reduces backend costs. 

ChatGPT is one example of fine-tuning. While GPT is the foundation model, ChatGPT is its fine-tuned counterpart adapted to generate text in a conversational style.

Retrieval Augmented Generation (RAG)

Often referred to as knowledge or prompt augmentation, RAG builds upon prompt engineering by supplementing prompts with information from external sources such as vector databases or APIs. This data is incorporated into the prompt before it’s submitted to the LLM. 

RAG is a more economic way to enhance the factual reliability of models without extensive model fine-tuning. 

What is Large Language Model Operations (LLMOps)?

In the rapidly evolving landscape of artificial intelligence (AI), LLMs like ChatGPT have emerged as groundbreaking tools. But anyone who has worked on AI systems using LLMs understands the distinct challenges of transitioning from a proof of concept in a local Jupyter Notebook to a full-fledged production system. 

LLMs introduce unique hurdles in development, deployment, and maintenance. As these language models grow in complexity and size, the necessity for efficient and streamlined operations becomes increasingly critical. 

That’s where LLMOps comes in. As a subset of MLOps, LLMOps is dedicated to overseeing the lifecycle of LLMs from training to maintenance using innovative tools and methodologies. By operationalizing technology at scale, LLMOps aims to make the path to adopting Generative AI easier.

How does LLMOps manage the lifecycle of a Large Language Model?

LLMOps equips developers with the essential tools and best practices they need to manage the development lifecycle of LLMs. Though many aspects of LLMOps mirror MLOps, foundation models require new methods, guidelines, and tools. 

Let's explore the LLM lifecycle with a focus on fine-tuning, as it's uncommon for organizations to train LLMs entirely from scratch.

In the fine-tuning process, you begin with an already trained foundation model. You then train it on a more specific, smaller dataset to create a custom model. 

After deploying this custom model, prompts are sent in, and the corresponding completions are returned. It's critical to monitor and retrain the model to ensure its performance remains consistent, especially for AI systems driven by LLMs. 

LLMOps facilitates the practical application of LLMs by incorporating prompt management, LLM chaining, monitoring, and observability techniques not typically found in conventional MLOps.

Prompt management 

Prompts are the primary means for people to interact with LLMs. Anyone who has crafted a prompt understands that refining it is a repetitive task that requires several attempts to attain a satisfactory outcome. 

Tools within LLMOps typically offer features to track and version prompts and their outputs over time. This makes it easier to gauge the model's overall efficacy. Certain platforms and tools also facilitate prompt evaluations across multiple LLMs so you can quickly find the best-performing LLM for your prompt.

LLM chaining

LLM chaining links multiple LLM calls in sequence to deliver a distinct application feature. In this workflow, the output from one LLM call serves as the input for the subsequent LLM call, culminating in the final result. This design approach introduces an innovative approach to AI application design and breaks down complex tasks into smaller steps. 

For instance, rather than employing a single extensive prompt to write a short story, you can break the prompt into shorter prompts for specific topics and receive more accurate results. 

Chaining combats the inherent constraint on the maximum number of tokens an LLM can process at once. LLMOps reduces the complexities involved in managing the chain and combines chaining with other document retrieval techniques like accessing a vector database. 

Monitoring and observability

An LLM observability system gathers real-time data points after the model deployment to detect potential degradation in model performance. Real-time monitoring enables timely identification, intervention, and correction of performance issues before they affect end users. 

Several data points are captured by an LLM observability system:

  • Prompts

  • Prompt tokens/length

  • Completions

  • Completion tokens/length

  • Unique identifier for the conversation

  • Latency 

  • Step in the LLM chain

  • Custom metadata

A well-structured observability system that consistently tracks prompt-completion pairs can pinpoint when modifications such as re-training or shifting foundation models begin to impact performance. 

It's also crucial to monitor models for drift and bias. While drift is a prevalent issue in conventional machine learning models, it’s even more important to monitor with LLMs due to their dependency on foundation models. 

Bias can emerge from the initial data sets the foundation model was trained on, the proprietary datasets used in fine-tuning, or even the human evaluators assessing prompt completions. To address bias effectively, a thorough evaluation and monitoring system is essential.

What are the differences between LLMOps and MLOps?

If you've made it this far, it's evident that LLMOps is the MLOps equivalent for LLMs. By now, you understand that LLMOps is critical to managing LLMs, especially fine-tuned LLMs you've trained. 

While LLMOps shares many similarities with MLOps, it's beneficial to compare their differences by looking at the typical tasks in the machine learning lifecycle.

What is the future of LLMs and LLMOps?

The rapid evolution of LLMOps tools and frameworks makes it challenging to forecast the technology's trajectory even a month from now, let alone a year. However, one thing is clear: LLMOps paves the way for enterprises to embrace LLMs. 

LLMs are transforming how we build AI-powered systems and broadening the accessibility to machine learning, turning AI into a mere prompt or API request. As Generative AI continues to develop, we're only beginning to uncover the potential of LLMs in addressing business challenges and streamlining operations. 

LLMOps is the optimal way to monitor and enhance the performance of LLMs over time, leading to faster resolution of performance issues and more satisfied customers. I'm eager to witness the evolution of LLMOps in the coming months and determine whether it will persist or transform into something entirely different! 

Diving into prompt engineering is a great starting point if you're just beginning with LLMs. Take a look at my course "Prompt Engineering for Improved Performance" to master advanced techniques in prompt engineering!

Kesha Williams

Kesha W.

Kesha Williams is an Atlanta-based AWS Machine Learning Hero, Alexa Champion, and Director of Cloud Engineering who leads engineering teams building cloud-native solutions with a focus on growing early career technologists into cloud professionals and engineering leaders. She holds multiple AWS certifications and has leadership training from Harvard Business School. Find her on Topmate at

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