It feels like we’ve hit an inflection point where the sheer volume of high-capability models releasing is actually slowing down my optimization loop. A few months ago, I had a pretty dialed-in workflow: one model for reasoning/architecture, another for pure code generation. The prompt engineering was stable, and I knew exactly where the hallucinations usually crept in. Now, with everything dropping at once (reasoning-specific variants, massive context windows, ultra-fast coding checkpoints), I find myself spending more time benchmarking and testing new endpoints than actually building. The specialized reasoning modes are incredible, but they require totally different prompting strategies than the standard high-token models. For those of you building agentic workflows or complex pipelines: Are you constantly refactoring your system prompts to chase the marginal gains of the newest release? Or have you just locked your version and decided to ignore the noise for a few months? I’m leaning towards the latter, but the FOMO on some of these reasoning capabilities is hard to ignore. Curious what the consensus is here. submitted by /u/HarrisonAIx
Originally posted by u/HarrisonAIx on r/ArtificialInteligence