This is the first in a multi-part series about Generative AI, focused on how to set up your Generative AI project for success. Whether you’re new to GenAI, or have your own tactics to share, there’s more we can all learn about implementing this new technology.

With the many offerings available now in the GenAI landscape, from OpenAI’s DALL-E and ChatGPT – already at a 4o version—to Meta’s LLaMA, to Microsoft’s Orca, to Google’s multiple AI offerings, Generative AI Large Language Models (GenAI LLM) now feels a bit inescapable. It can be easy to get caught up in the excitement about adding a GenAI LLM-enabled tool to your company’s portfolio, but it can be difficult to know where to start, and what needs to be in place to succeed. So before we discuss the various offerings or how to implement LLMs, let’s take a look at how you can set your team up for success—whether you’re in Engineering, Product or Design—before embarking on your next GenAI LLM project.

1. Know what you can change with LLMs – and consider how you can change the rest 
   This is a question we think about all the time at Grand Studio: what problems can – and should – be solved with a given technology? With technologies as complex as LLMs that involve trillions of tokens, years of training, and millions of dollars, designing a new LLM might be a bit out of reach for many. But even for those who can access these solutions, it still doesn’t mean that all aspects of their problems should be solved with a GenAI modality. That’s why exploring what the problems are, how users behave and what tools they use, as well as what combination of solutions may most holistically address the issue(s) is an important first step. And if a GenAI LLM is in fact the right solution,  there may still be quite a few elements of a problem  that can be solved for and improved outside of a GenAI. 
   
   One recent example came up as we were designing a GenAI LLM solution: one of the use cases we wanted to tackle had to sit outside the solution’s access point due to security measures and therefore could not be addressed by the GenAI. We were able to do a UX/UI heuristic pass and create a set of digital UX adjustments that reduced the issues with that use case so much that the amount of money the enterprise was spending dropped an entire contract tier.  So don’t underestimate the impact of UX/UI within a holistic solution.

2. Clean up your data
   We’ve said it before and we’ll say it again: your GenAI will flourish or fail depending on how clean and organized your data set is. The general data sets that inform current LLMs are massive and in order to get answers that are relevant and accurate for your company, or even your industry, you’ll likely need to help the LLM focus in some way. Data lakes – essentially centralized areas for your data that an LLM can be required to check first before generating answers, and carefully crafted back-end directions on what information to present – and how – (called system prompts) can help your LLM prioritize certain data before going into its general knowledge. The trick is that data has to be organized, well-written, and clean of errors first. This can be a big ask if you are the kind of company that has a huge knowledge base archive that maybe hasn’t been overhauled in years.
   
   One way to tackle this is to start small(er). You won’t be able to get away with only 100 clean pieces of data, but you might be able to get away with ~ 1000. Starting small and establishing a content governance structure can help you out in the long run, as knowledge becomes more relevant and up to date, both for your new GenAI buddy and for the employees in the business itself. (And if content governance is new to you, that’s something that consultancies like Grand Studio can help with.)

3. Testing, testing, testing
   GenAI is a new – and therefore unpredictable – technology. People have a lot of mixed feelings about GenAI; some people are excited about what they see as a tool of the future, while others are skeptical or even afraid of what GenAI will mean for their job security and place in the workforce. Building multiple moments of user-centered research and  testing into your project plan can help you build empathy with your target audience, with an added benefit of not only spotting technical bugs and glitches, but also helping people start to build trust and understanding of what this technology is capable of. Thorough research with the right users can also help your internal comms or external product marketing teams create a finely-tuned product launch messaging and rollout plan. (As it happens, Grand Studio is so committed to user-centering all products and services that we’ve created a public-facing framework to help put this into action).

4. Embrace the whimsy
   Finally, as you’re gearing up to get started on your exciting new GenAI-enabled product, it’s important to set some grounded expectations and cut through the marketing hype. GenAI, and LLMs in particular, are not silver bullets. They are emerging technologies that are still being experimented on, developed, and tested out every day. There are limited functionalities as to what these LLMs are capable of; they’re not truly “intelligent” and they can’t read your – or your users’ – minds. And there’s still a learning curve to understanding how to get the best out of these technologies.
   
   Bias and hallucinations are real risks that could open you and your company up to potential liability depending on your target audience and industry. Company security is an additional concern given that data once fed into an LLM – even a company’s proprietary LLM or Wrapper – is impossible to remove once it’s in there so there will need to be additional protections in place. Having these hard conversations about why your solution should include a GenAI-enabled product and what the expectations of this technology are before you get started will save everyone a lot of time and pain later on as these limitations make themselves known.

Overall, GenAI is an exciting thing that has a whole world of potential and possibilities attached to it. We believe that being honest about the technology’s limitations and setting yourself up for success as best as possible will give you the greatest chance to make the best use of this emerging technology and its capabilities.

Stay tuned for the next part of this series: The Ideal GenAI Design Process