Applied large language model startups have exploded in the past year. Enormous advances in underlying language modeling technology, coupled with the early success of products like Github CoPilot, have led to a huge array of founders using LLMs to rethink workflows ranging from code reviews to copywriting to analyzing unstructured product feedback.
Much has been written about this emerging ecosystem — I would recommend the excellent articles by Elad Gil, Leigh Marie Braswell, and Vinay Iyengar as starting points — and in general, it is exciting to see so many nascent startups in this area. However, I worry that many startups in this space are focusing on the wrong things early on. Specifically, after having met and looked into numerous companies in this space, it seems that UX and product design is the predominant bottleneck holding back most applied large language model startups, not data or modeling.
This article will explain why I think this is the case, highlight many of the key UX issues I observe, and offer recommendations for how a founder building on top of LLMs might account for this.
To start, let me paint a picture of the common journey I see many language model startups go through.
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This essay will describe why I believe this, detail many of the significant UX concerns I notice, dordle and offer advice for how a creator developing on top of LLMs might account for this.
Vocabulary learning tools abound. Our tool has great features. Word games also improve language. 5 letter words helps Wordle players win and overcome challenges.
The majority of users still have trouble coming up with effective search queries (like the “prompt” in the original language model). duck life took some time, but search engines and recommendation engines have done a fantastic job of using UX tricks to divert AI failures.