I thought model quality was the bottleneck. It wasn’t ""I used to think the main problem was just picking the “best” model, so I did what most people probably do: run the same prompt through GPT, Claude, sometimes Gemini, compare the outputs, and pick the one that feels right. It worked fine at first, however, I’ve found that asking the same LLM question can sometimes yield different results. And over time it started to feel like half my workflow was just evaluating AI instead of actually getting work done. What changed for me wasn’t switching to a better model, it was trying a different setup. So I‘ve started to messed around with tools like Genspark a bit because I noticed it doesn’t really force you to commit to one model. It can route the same task across different models and then kind of consolidate the results. It’s not perfect, but it felt much closer to how I was already working, just without the manual back-and-forth. Made me realize the bottleneck was never the model itself, it was the process around it. submitted by /u/cosamueldavid
Originally posted by u/cosamueldavid on r/ArtificialInteligence
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