LLMOps: What is Often Forgotten in the Rush to LLMs

Enterprises are embracing generative AI and utilizing or building large language models (LLMs) to support AI applications. But despite all this excitement and activity, few companies are addressing a crucial topic — large language model operations (LLMOps). 

LLMOps tools are vital to keeping knowledge bases and fine-tuning model data up to date. But many businesses and users haven’t thought that far ahead. They’re simply trying to understand LLMs and put them to use. Without a change in perspective, their models will deliver diminishing returns over time. 

Zooming out, LLMOps addresses the question, “What does it even mean to have a long-term generative AI solution?” If the world is constantly changing, then your model is constantly going out of date. Are you prepared to adjust? Or will updating your models to maintain high performance be a constant grind because you haven’t planned ahead?

Learn more about what LLMOps is and why it’s so valuable for long-lasting, effective generative AI solutions. We’ll also offer advice on LLMOps implementation and how to tailor models to meet your organizational needs and strategy.

What Is LLMOps?

LLMOps, short for large language model operations, is a discipline within the broader field of MLOps that focuses on managing the lifecycle of large language models (LLMs). LLMs are capable of many tasks including text summarization, translation, and conversational responses. These models must be regularly maintained to generate accurate and reliable outputs.

LLMOps provides a framework to manage and maintain LLMs over time to keep them performing optimally. This involves updating underlying data and knowledge bases, as well as potentially fine-tuning the models themselves. LLMops also includes automating workflows for updating data and knowledge bases, as well as retraining models.

Similar to machine learning operations (MLOps), LLMOps focuses on language-specific considerations and continuously updating and maintaining models to provide the most accurate results. Just as a CI/CD pipeline is essential for application development, keeping LLMs fresh and up to date is crucial for obtaining reliable and relevant outputs. 

Understanding the LLM Lifecycle  

LLMOps starts with understanding that LLMs have a finite lifetime and need monitoring and maintenance throughout. Managing this lifecycle requires evaluating when to fine-tune models or update the underlying knowledge base. That’s where LLMOps comes in — helping organizations automatically refresh and update knowledge bases, reduce manual work and keep models trained, tested and validated. 

For instance, when the COVID-19 pandemic struck, ML models across industries became obsolete, especially those operating in housing and healthcare. The models required updated data to reflect the rapidly changing environment. But the circumstances don’t need to be that harrowing — product launches or feature updates can also leave existing models or databases over-reliant on outdated or irrelevant data. 

The challenge is that there is inherent complexity and difficulty in managing and fine-tuning LLMs. You’re often dealing with black boxes or trying to force data into specific formats. This complexity can deter an organization from fully embracing generative AI. That’s why a robust LLMOps approach is crucial to keeping data up to date so your data scientists can focus on more impactful work.

5 Ways to Align LLMs With Your Organization’s Strategic Needs

Relying solely on off-the-shelf LLMs limits the benefits of this technology. Models and knowledge bases become more powerful when you add business-relevant data and shape model behavior to align with strategic goals, business requirements, or use cases. 

Let’s review steps that give businesses the flexibility to customize, update and refine models, creating long-term alignment even as strategic goals change.

Monitoring and Observability

Monitoring LLM performance ensures they are functioning well and flags issues if they occur.
With the right tools and practices, LLMOps teams can track key metrics related to LLM performance, such as error rates, latency, and usage. These metrics illustrate the model's behavior and can help identify potential bottlenecks or areas for improvement.

Observability also goes beyond monitoring to extract insights from the inner workings of LLMs. LLMOps tools provide visibility into the model's internal processes, such as token usage and response times. This gives organizations a better understanding of functionality, costs, and opportunities for performance improvement.

Prompt Engineering

Prompt engineering provides instructions for specific tasks or desired outputs, tailoring LLMs to align with strategic objectives or use cases. Consider a simple prompt example like "Question: What is the return policy? Answer: Our return policy allows customers to return products within 30 days of purchase for a full refund or exchange." Appending examples like these in front of a prompt is called “few-shot prompting,” relating to the number of “shots” you give a model through examples to help it emulate a similar pattern in its responses. Appending prompts in this way can help an LLM better understand context when responding to customer inquiries, for example. 

Prompt engineering also enables organizations to iterate and refine their prompts based on feedback and real-world performance. Continuously evaluating and modifying prompts can enhance the accuracy, relevance, and effectiveness of the generated outputs. It has been speculated that the large model makers do exactly this in their web interfaces when handling more complex questions, which is part of the reason the apparent intelligence of these models continues to grow.

As the number of projects using LLMs in your organization grows, it's important to catalog and version all engineered prompts. This allows for easy reuse of prompt templates across projects and easy reversion to earlier templates if performance ever degrades when making a change. Monitoring for drift over time becomes easier, too.

Fine-Tuning

Fine-tuning involves training LLMs on curated datasets to enhance their performance and adapt them to specific use cases. There are two main approaches to fine-tuning: full fine-tuning and parameter-efficient fine tuning (PEFT). Full fine-tuning requires many examples and significant computational power to tune the model’s weights and biases. PEFT can be a cost-effective alternative, as it requires fewer examples and less computing resources. 

Fine-tuning provides domain-specific data and examples related to your use case, which improves a model’s ability to generate accurate and contextually appropriate responses. You can use this approach to address biases or tailor to industry/use case requirements, too, and doing so can improve strategic alignment and reduce legal or regulatory risks.

Retrieval Augmented Generation

RAG uses semantic search to retrieve relevant and timely context, augmenting an LLM's generated responses for more accurate and context-aware results. This technique involves creating embeddings and setting up vector databases to store the contextual information. 

AWS offers several options for vector stores, including Amazon Kendra, which you can use to create embeddings and perform semantic search. RAG can improve flexibility and cost-effectiveness when combined with fine-tuning.

Governance and Compliance

Incorporating governance and compliance into LLMOps ensures that models align with organizational priorities and ethical and legal standards. By implementing guardrails, organizations can guide user interactions and prevent misuse. For instance, a healthcare chatbot can be configured to focus solely on health-related inquiries, avoiding the potential pitfalls of venturing into unrelated or sensitive topics.

3 Considerations When Implementing LLMOps 

Processing & Conversational Memory

Processing refers to the computational effort of parsing the user’s natural language input and generating output. Every call to an LLM incurs this expense, either in terms of per-token API costs or just computational costs, which is why minimizing the calls made to an LLM is a best practice.

Processing can be minimized by creating a conversational memory for the other parts of your solution to refer to. This makes any output a model generates accessible without requiring additional calls to the model. And while this type of “short-term” memory already exists within a model’s context window—the total amount of the conversation it can store—solution performance is aided by also developing a “long-term” memory and storing conversations in other AWS storage, like Amazon DynamoDB, for instance. This also benefits the latency of these solutions, by requiring fewer round trips between the model and the other parts of your solution.

By querying from this kind of alternative storage as much as possible, you can significantly reduce processing costs. And by using an orchestrator (like LangChain) in your solution, you can also check user prompts against this conversational memory, intercept repeat queries, and retrieve previously generated answers.

Performance

While traditional ML problems can use metrics like accuracy, precision, and recall to measure performance, LLM evaluation may require additional metrics. These include CIDEr and SPICE, which evaluate the similarity and quality of generated captions for images.

Universal benchmarks like GLUE, SuperGLUE, and Big Bench Collaboration Benchmarks provide standardized evaluation metrics for tasks such as machine translation, summarization, natural language generation and question answering. Code-specific benchmarks like HumanEval and Mostly Basic Python Programming (MBPP) focus on specific domains.

While automated model monitoring is crucial, human evaluation is sometimes necessary. In such instances, a human-in-the-loop (HITL) setup can be implemented, in which human evaluators assess LLM-generated text for quality. This human feedback can be especially valuable when automated monitoring or relevant benchmarks aren't available.

Hosting

Hosting LLMs involves handling the computational requirements, managing the model's resources and ensuring efficient inference latency and throughput. Cost management is important, as an increasing user base can raise the cost of maintaining optimal performance.

Traditional hosting solutions can’t always host LLMs with optimal performance. For businesses looking for long-term solutions for generative AI and MLOps, AWS SageMaker offers many solutions.

Companies can also consider self-hosting solutions or leveraging API layers such as Bedrock. These methods offer more control over costs and performance, especially when scaling users.

Developing a Long-Running Generative AI Solution

As you develop generative AI solutions, getting LLMs up and running is just the first step. Long-term success depends on your ability to maintain, optimize and adapt. With LLMOps, organizations can optimize their LLMs, ensuring they perform consistently on day one, day 100 and beyond.

You don’t have to manage LLMOps on your own, especially with your team facing countless other priorities. Turn to experienced professionals, like Mission Cloud, who have a deep understanding of LLMOps and can navigate the complexities with ease.

With proven experience and in-depth knowledge of generative AI, Mission Cloud is equipped to help your business turn a concept into a long-running generative AI solution. Our team of experts understands the nuances of LLMOps and can guide your organization through the entire process.

Have any generative AI, LLMOps or other data, analytics or machine learning questions? Get in touch to learn how to make the most of your data with Mission Cloud.