Summary
In this chapter, we provided a comprehensive overview of the generative AI project lifecycle, from identifying business use cases to model deployment. We explored major generative technologies like FMs and key techniques for customization including domain adaptation, instruction tuning, reinforcement learning with human feedback, and prompt engineering.
The chapter also covered specialized engineering considerations around large model hosting and mitigating risks like factual inaccuracies. While limitations exist, responsible development and governance can allow enterprises across industries to harness generative AI’s immense potential for creating business value. With an understanding of the end-to-end lifecycle, practitioners can thoughtfully architect and deliver innovative yet practical generative AI solutions.
In the next chapter, we will talk about the key considerations for building a generative AI platform, retrieval-augmented generation (RAG) solutions...
