What do you think AI still struggles with the most?

Yeah, it's strange—sometimes AI falls down on stuff that feels like common sense. I've spent a ton of time over the past 12 months using LLMs to do categorization. What I've learned is that you can get 80% results pretty easily, but if you need 99%, it's nearly impossible. Take a simple example, such as categorizing products in Product Hunt to separate B2C and B2B SaaS based on description. This will require extensive experimentation just to get to 80%. Some observations: 🤷‍♂️ Giving it lots of descriptive categories can be better than giving it a binary choice. Rather than just "B2B" and "B2C," you'll probably see better results with concrete categories (a long list of "Business scheduling software", "Personal scheduling software", etc). 💭 Pseudo chain-of-thought can help. For example, having the AI generate a thought about what category something might be and why, then having it down select to a few candidates, then have it finally make its selection (all in the same response). ⚖️ Occasionally adding a "judge" pass can help. The first LLM makes the call, and the second one critiques it. It can help to have the judge generate arguments for & against before rendering its verdict. 💡 Another approach is to have tell the LLM to categorize the product, but don't give it the categories. Then have a second LLM map the generated category to a list of valid categories (something it does pretty well). Anyway, I guess the takeaway is don't get me started on categorization. 😅