The generative AI application lifecycle (LLMOps)

Who this is for and what you will get

This guide is for developers who have built a first LLM prototype and want to turn it into a stable product, and for people who plan AI projects. By the end you will be able to name the stages of the generative AI lifecycle, know what each stage involves and which metrics measure it, and see where PromptFlow and Microsoft Foundry help.^[1]

Why an application needs a lifecycle

AI is a fast-moving field. An AI application that is not monitored, evaluated and improved after launch can quickly lose relevance and become unreliable. The generative AI lifecycle is a framework for developing, deploying and maintaining a generative AI application.^[1]

It helps you define goals, measure performance, identify challenges and implement solutions. It also keeps the application aligned with the ethical and legal standards of its domain and its stakeholders. Following it is how an application keeps delivering value and satisfying users.^[1]

A one-off experiment does not need every stage in full. An application that serves users over time cannot skip monitoring, evaluation and improvement, so the lifecycle is a basis for trade-offs rather than a checklist to finish once.^[1]

From MLOps to LLMOps

Older AI applications can be called “ML Apps”, and newer ones “GenAI Apps” or “AI Apps”. The name change reflects the technology in use.^[1]

LLMs are a new tool. They are very capable at analysis and generation, but they change how AI work is organized. Compared with MLOps, LLMOps focuses more on app developers, treats integrations as a key point, and uses Models-as-a-Service.^[1]

The LLM lifecycle adds new requirements: prompting, techniques that improve quality such as fine-tuning, RAG and meta-prompts, responsible AI assessment, and new evaluation metrics.^[1]

For newcomers, the difference can be stated simply. MLOps is mostly about training and deploying your own model, with the data pipeline and training results at the centre. LLMOps is mostly about building an application on models that someone else provides, so prompts, integrations, quality and cost get most of the attention.^[1]

Five metrics for LLM applications

Five metrics can serve as a checklist before and after launch:^[1]

• Quality: how good the responses are.^[1]
• Harm: whether the application meets Responsible AI requirements.^[1]
• Honesty: whether responses are grounded, and whether they make sense and are correct.^[1]
• Cost: the solution budget.^[1]
• Latency: the average time to produce a token response.^[1]

Three stages: explore, build, operationalize

The steps are not linear. They form integrated, iterative loops, all inside an overarching cycle. Three big stages help you see the shape of the process.^[1]

1. Ideating/Exploring: explore possibilities according to business needs. Prototype by creating a PromptFlow, and test whether the approach is efficient enough to support your hypothesis.^[1]
2. Building/Augmenting: evaluate the solution on bigger datasets, and implement techniques such as fine-tuning and RAG to check robustness. If the results fall short, re-implement, add steps to the flow or restructure the data. Move on only when the flow and scale work and the metrics are met.^[1]
3. Operationalizing: add monitoring and alert systems, deploy, and integrate the application.^[1]

Management is the overarching cycle, focused on security, compliance and governance.^[1]

Think of the three stages as one loop that keeps turning: exploration decides what to build, building decides whether it is good enough, and operationalizing decides whether it can serve users reliably. Management keeps the whole loop secure and compliant. After launch, monitoring and alerts help you spot a drop in quality or a rise in cost, so the team can return to building in time.^[1]

Tools for the lifecycle

Microsoft provides the Azure AI Platform and PromptFlow to make the cycle easier to put into practice.^[1]

The Azure AI Platform gives access to Microsoft Foundry, formerly Azure AI Studio. Foundry is a web portal for exploring models, samples and tools and managing resources. It supports UI development flows, and SDK and CLI options for code-first development. Azure AI also lets you use multiple resources to manage operations, services, projects, vector search and databases.^[1]

PromptFlow takes an application from proof of concept to large scale. A typical sequence is:

1. In VS Code, design and build the app with visual and functional tools.^[1]
2. Test and fine-tune the app to improve the quality of its AI output.^[1]
3. Use Microsoft Foundry to integrate and iterate with the cloud, then push and deploy for quick integration.^[1]

If you only want to test an idea, start with a PromptFlow prototype in VS Code and do not deploy to the cloud yet.^[1]

Limits and common questions

The lifecycle is a framework, not a fixed list of steps. Teams that read it as a strict sequence often find that conclusions from exploration get overturned during building, and they have to loop back. That iterative character is the point.^[1]

The metrics also trade off against each other. A more accurate answer may cost more or take longer, so review the five metrics together rather than optimizing one number.^[1]

In practice, set an acceptable range for each metric, and measure again after every change to a prompt or a model. That shows whether a change improved quality or only raised cost.^[1]

Next steps

For a hands-on look at an application built around these concepts, open the Contoso Chat Demo.^[1]

Use the demo to see how the concepts are organized in a complete application, and match each part of it to one of the three stages.^[1]

The next topic is Retrieval Augmented Generation (RAG) and vector databases, which affect how well generative AI applications perform.^[1]

Adapted from lesson 14 of Microsoft's open-source course *Generative AI for Beginners*^[1], released under the MIT License.