Advance your maturity level for Generative Artificial Intelligence Operations (GenAIOps)

Generative Artificial Intelligence Operations, or GenAIOps (sometimes called LLMOps), describes the operational practices and strategies for managing large language models (LLMs) in production. This article provides guidance on how to advance your capabilities in GenAIOps, based on your organization's current maturity level.

Diagram shows maturity level of GenAIOps.

Use the descriptions below to find your GenAIOps Maturity Model ranking level. These levels provide a general understanding and practical application level of your organization. The guidelines provide you with helpful links to expand your GenAIOps knowledge base.

Tip

Use the GenAIOps Maturity Model Assessment to determine your organization's current GenAIOps maturity level. The questionnaire is designed to help you understand your organization's current capabilities and identify areas for improvement.

Your results from the assessment corresponds to a GenAIOps Maturity Model ranking level, providing a general understanding and practical application level of your organization. These guidelines provide you with helpful links to expand your GenAIOps knowledge base.

Level 1 - initial

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Score from GenAIOps Maturity Model Assessment: initial (0-9).

Description: Your organization is at the initial foundational stage of GenAIOps maturity. You're exploring the capabilities of LLMs but haven't yet developed structured practices or systematic approaches.

Begin by familiarizing yourself with different LLM APIs and their capabilities. Next, start experimenting with structured prompt design and basic prompt engineering. Review Microsoft Learning articles as a starting point. Taking what you’ve learned, discover how to introduce basic metrics for LLM application performance evaluation.

Suggested references for level 1 advancement

To better understand GenAIOps, consider available MS Learning courses and workshops.

Level 2 - defined

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Score from GenAIOps Maturity Model Assessment: maturing (10-14).

Description: Your organization has started to systematize LLM operations, with a focus on structured development and experimentation. However, there's room for more sophisticated integration and optimization.

To improve your capabilities and skills, learn how to develop more complex prompts and begin integrating them effectively into applications. During this journey, you’ll want to implement a systematic approach for LLM application deployment, possibly exploring CI/CD integration. Once you understand the core, you can begin employing more advanced evaluation metrics like groundedness, relevance, and similarity. Ultimately, you’ll want to focus on content safety and ethical considerations in LLM usage.

Suggested references for level 2 advancement

Level 3 - managed

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Score from GenAIOps Maturity Model Assessment: maturing (15-19).

Description: Your organization is managing advanced LLM workflows with proactive monitoring and structured deployment strategies. You're close to achieving operational excellence.

To expand your base knowledge, focus on continuous improvement and innovation in your LLM applications. As you progress, you can enhance your monitoring strategies with predictive analytics and comprehensive content safety measures. Learn to optimize and fine-tune your LLM applications for specific requirements. Ultimately, you want to strengthen your asset management strategies through advanced version control and rollback capabilities.

Suggested references for level 3 advancement

Level 4 - optimized

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Score from GenAIOps Maturity Model Assessment: optimized (20-28).

Description: Your organization demonstrates operational excellence in GenAIOps. You have a sophisticated approach to LLM application development, deployment, and monitoring.

As LLMs evolve, you’ll want to maintain your cutting-edge position by staying updated with the latest LLM advancements. Continuously evaluate the alignment of your LLM strategies with evolving business objectives. Ensure that you foster a culture of innovation and continuous learning within your team. Last, but not least, share your knowledge and best practices with the wider community to establish thought leadership in the field.

Suggested references for advanced techniques