Let’s talk about design for AI (which more and more, I’m agreeing means GenAI to those outside the data space). The hype around GenAI and LLMs—particularly as it relates to dropping these in as features into a software application or product—seems to me, at this time, to largely be driven by FOMO rather than real value. In this “part 1” episode, I look at the importance of solid user experience design and outcome-oriented thinking when deploying LLMs into enterprise products. Challenges with immature AI UIs, the role of context, the constant game of understanding what accuracy means (and how much this matters), and the potential impact on human workers are also examined. Through a hypothetical scenario, I illustrate the complexities of using LLMs in practical applications, stressing the need for careful consideration of benchmarks and the acceptance of GenAI's risks.
I also want to note that LLMs are a very immature space in terms of UI/UX design—even if the foundation models continue to mature at a rapid pace. As such, this episode is more about the questions and mindset I would be considering when integrating LLMs into enterprise software more than a suggestion of “best practices.”
Highlights/ Skip to:
- (1:15) Currently, many LLM feature initiatives seem to mostly driven by FOMO
- (2:45) UX Considerations for LLM-enhanced enterprise applications
- (5:14) Challenges with LLM UIs / user interfaces
- (7:24) Measuring improvement in UX outcomes with LLMs
- (10:36) Accuracy in LLMs and its relevance in enterprise software
- (11:28) Illustrating key consideration for implementing an LLM-based feature
- (19:00) Leadership and context in AI deployment
- (19:27) Determining UX benchmarks for using LLMs
- (20:14) The dynamic nature of LLM hallucinations and how we design for the unknown
- (21:16) Closing thoughts on Part 1 of designing for AI and LLMs
Quotes from Today’s Episode
- “While many product teams continue to race to deploy some sort of GenAI and especially LLMs into their products—particularly this is in the tech sector for commercial software companies—the general sense I’m getting is that this is still more about FOMO than anything else.” - Brian T. O’Neill (2:07)
- “No matter what the technology is, a good user experience design foundation starts with not doing any harm, and hopefully going beyond usable to be delightful. And adding LLM capabilities into a solution is really no different. So, we still need to have outcome-oriented thinking on both our product and design teams when deploying LLM capabilities into a solution. This is a cornerstone of good product work.” - Brian T. O’Neill (3:03)
- “So, challenges with LLM UIs and UXs, right, user interfaces and experiences, the most obvious challenge to me right now with large language model interfaces is that while we’ve given users tremendous flexibility in the form of a Google search-like interface, we’ve also in many cases, limited the UX of these interactions to a text conversation with a machine. We’re back to the CLI in some ways.” - Brian T. O’Neill (5:14)
- “Before and after we insert an LLM into a user’s workflow, we need to know what an improvement in their life or work actually means.”- Brian T. O’Neill (7:24)
- "If it would take the machine a few seconds to process a result versus what might take a day for a worker, what’s the role and purpose of that worker going forward? I think these are all considerations that need to be made, particularly if you’re concerned about adoption, which a lot of data product leaders are." - Brian T. O’Neill (10:17)
- “So, there’s no right or wrong answer here. These are all range questions, and they’re leadership questions, and context really matters. They are important to ask, particularly when we have this risk of reacting to incorrect information that looks plausible and believable because of how these LLMs tend to respond to us with a positive sheen much of the time.” - Brian T. O’Neill (19:00)
Links
Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)