What Hat Should the LLM Wear?

Early research trying to test if entrepreneurial training improved micro, small, and medium-sized enterprise (MSME) performance suffered from a commom problem: what type of training? Training could be via lectures in a workshop, or individualized mentorship and consulting. It could focus on hard business skills, or on psychological traits like personal initiative. It could be a few hours with limited attendance, or an intensive in-depth process. After billions was spend training millions of entrepreneurs, best practices emerged, and it turned out that these design differences mattered quite a bit for impact. 

Similarly, if we want to know if AI-enabled training, mentoring and consulting can improve MSME performance, the first question is what kind? An LLM-based tool could actually wear many hats. It could be the expert on a hill that entrepeneurs come to with their own questions, and which dispenses detailed information based on best practies. It could be more like a personal trainer, working with the entrepreneur to identify an improvement they can implement immediately for a quick win, and motivating them towards action. It could be like a career coach, taking the entrepreneur through a long-term goal setting exercise and building a top-down plan from there. It could follow the entpreneur's priorities, or use training materials and other users' queries to guide towards a broader set of topics. Becuase AI-based entrepreneurial training is so new, its not clear what hat the LLM should wear. 

In our early results, we're finding that rather than picking one approach, different entrepreneurs in different situations prefer a different mix of these approaches. And it is a mix rather than a single one- most of our early users have some business priorities of their own they want to address first, and then appreciate suggestions of new areas of inquiry. And after an initial testing of the system with detailed substantive questions, the key seems less about transferring knowledge and more about helping put knowledge into practice. Given how quickly AI capabilities are evolving, there are probably many new hats we haven't even considered yet. That is why our focus this year is offering and testing a variety of approaches and tools before focusing on impact evaluation and scale-up, and we're keen to share our ideas and to hear from others, so we can all figure out what works.