LLMOps best practices

As we wrap up this final section, we know that successfully navigating the generative AI and LLM landscape requires effective practice. As this generative AI space is still fairly new and ever-growing, so are the lessons learned and the list of best practices being enhanced. We provide some guidelines to follow for some effective LLMOps practices:

• Build for the enterprise and build for scalability: To ensure smooth deployment and growth, organizations should build around enterprise-ready tooling and enterprise-class infrastructure for their LLMOps requirements. Fortunately, many hyperscale cloud vendors make this very simple, as you can build your generative AI applications and services using tested and proven methodologies. Additionally, these hyperscale cloud vendors provide the proper security and guardrails to make your generative AI project a success. We will be going into the enterprise-ready, scalable environments in the next chapter.

• Remain flexible and use agility: The world’s journey into LLMOps has just started. We did provide details of this in this chapter, yet with new innovations and challenges, it is essential to remain flexible and evolve as we have this major paradigm shift. Develop an LLMOps strategy, based on the concepts and techniques you have learned in this chapter, yet do not remain rigid as this strategy will also need to evolve as the LLM/generative AI technology evolves.

• Focus on data quality: A data quality focus means putting resources into reliable data, applying solid data management practices, and adopting solid review practices. Organizations need to use high-quality data that is relevant, accurate, and unbiased to train and fine-tune LLMs properly. This is also incorporated into the LLM lifecycle phases you learned earlier in this chapter. Also, it is almost given that organizations use version control and deploy using standardized development tooling and clean data pipelines to prepare and manage the data, so having quality data is a must.

• Improve experimentation while making enhancements: The LLMOps lifecycle, including LLM development and deployment, is ongoing. There is a constant demand for new data and behavior improvements and enhancements. Most all of the tooling for experimentation and making enhancements can be automated, however always keep a human-in-the-loop for the quality control and alignment with business outcomes.

Summary

In this chapter, we covered the basis of generative AI intersecting with software development. We covered three popular programming generative AI application frameworks: Semantic Kernel, LangChain, and LlamaIndex. We also introduced LLMOps, a comprehensive framework for managing the lifecycle of a generative AI ecosystem and how Prompt Flow can simplify the management of an LLMOps strategy; together, all of these components form a comprehensive framework for developing and deploying generative AI applications and services.

We also described the lifecycle of an LLM model itself to round out the lifecycle discussion.

As we look at extensibility and automating, we delved into the world of agents and autonomous agents, such as AutoGen and AutoGPT, which can work autonomously to address extremely complex problems by using a few techniques such as chaining or networking LLMs together in collaboration.

Finally, we looked at an actual case study of a large organization and how they adopted LLMOps.

From this, we wrapped up the chapter with some LLMOps best practices.

While the landscapes of programming language frameworks, tools, and agents are constantly being enhanced on an almost daily basis, we can all agree that the concepts you have learned thus far pave the way for enterprises to embrace generative AI and LLMs and be able to manage and operationalize the tooling and process easily and at scale.

Now that we have a clearer picture of how LLM models and LLM-based applications are created using programming language frameworks and made more efficient by using LLMOps, let’s slightly change our focus for the next chapter. In the next chapter, let’s expand more on the operational side of the cloud and expand our understanding of how LLM models, such as ChatGPT, are deployed at a large scale from an architecture design perspective. We will also understand the scaling strategies used in the cloud for such large deployments.