A Perspective on Generative AI from an ML Product Lead: Best Practices and Considerations

Generative AI (GenAI) and consequently large language models (LLMs) have gained significant <a href="https://www.nytimes.com/2023/03/28/technology/ai-chatbots-chatgpt-bing-bard-llm.html" rel="noopener ugc nofollow" target="_blank">traction</a> and <a href="https://www.economist.com/interactive/science-and-technology/2023/04/22/large-creative-ai-models-will-transform-how-we-live-and-work" rel="noopener ugc nofollow" target="_blank">attention</a> in the enterprise space this year. The hype promises transformative applications that are flexible to various industries and highly capable. As someone who has supported “conventional” ML product discovery, design, tuning, deployment, and monitoring in production, I am excited by the prospect of highly flexible functionality like GenAI. However, I believe many of the <a href="https://medium.com/slalom-data-ai/where-should-a-ceo-be-focusing-now-for-future-generative-ai-success-37934ac8ff0" rel="noopener">challenges</a> and hurdles that conventional ML has historically faced in enterprise deployments remain and intersect with all GenAI use cases. By incorporating best practices and asking key questions at each stage of the development and deployment process, we can mitigate risks and maximize the benefits of GenAI. I will mention that this article does not focus on GenAI and LLM capabilities, as there is already a wealth of <a href="https://www.gartner.com/en/topics/artificial-intelligence" rel="noopener ugc nofollow" target="_blank">credible material</a> on that topic. However, as an organizational leader, we encourage you to build a <a href="https://arxiv.org/pdf/2303.18223.pdf" rel="noopener ugc nofollow" target="_blank">foundational understanding </a>of GenAI capabilities, such as fine-tuned quality assurance, documentation search, concept parsing, code development support, code debugging, text summarization, and more. Additionally, it is crucial to understand the nature of ML and GenAI outputs as a “prediction” or a best guess, which can range from being perfectly right to somewhat right or even not close at all. This spectrum of performance and its correlation to expectations of success and risk are crucial considerations. GenAI have traction because they get “close to right” often enough, with little effort, to justify the excitement. <h1>Best practices and key questions</h1> <h2>Consider the hype cycle and let outcomes drive the solution not the tool.</h2> The hype surrounding GenAI can be both valuable and detrimental. On one hand, hype brings attention to new tools and fosters a willingness across stakeholders to adopt them, leading to exploration, experimentation, and refinement for new use cases. On the other hand, hype also invites critique, sometimes to the point of aversion, and necessitates a thorough examination of the landscape of evidence and opinions regarding a tool like GenAI. As adopters of GenAI, it is crucial that we navigate the hype cycle carefully and leverage it to drive meaningful impact while managing risks in our organizations. <a href="https://medium.com/slalom-data-ai/a-perspective-on-generative-ai-from-an-ml-product-lead-best-practices-and-considerations-95688d6a012d">Visit Now</a>