The tasks these methods are tackling are generally significant and realistic. Think complex QA like HotPotQA or Google-Proof QA, math reasoning (GSM8K), coding challenges, and even agentic systems. It's not just about toy problems anymore.

Are the improvements robust? It's an evolving space, but the big win seems to be for smaller, open-source LLMs. These techniques can genuinely uplift them to near the performance of larger, proprietary models, which is massive for cost reduction and accessibility. For already SOTA models, the headline metric gains might be smaller single-digit percentages on very hard tasks, but this often translates into crucial improvements in reliability and the model's ability to follow complex instructions accurately.

"Textual gradient"-like mechanisms (or execution traces, or actual gradients over reasoning as in some newer work ) are becoming essential. Manually fine-tuning complex prompt workflows or prompts with many distinct nodes or components just doesn't scale. These automated methods provide a more principled and systematic approach to guide and refine LLM behavior.

So, less "spectacular" gains on the absolute hardest tasks with the biggest models, yes, but still valuable. More importantly, it's a powerful optimization route for making capable AI more efficient and accessible. And critically, it's shifting prompt design from a black art to a more transparent, traceable, and robust engineering discipline. That foundational aspect is probably the most significant contribution right now.