"Self Learning GPTs" is a demo app designed to demonstrate how applications can improve over time through feedback collection and utilization. Utilizing LangSmith, the app captures feedback and creates few-shot examples to enhance prompts, thereby facilitating automatic learning and improvement. Learning from feedback has long been a critical component in developing LLM systems, as evidenced by models like ChatGPT and Midjourney, which utilize feedback loops to optimize performance. LangSmith emphasizes feedback collection by allowing users to programmatically log feedback and attach it to specific runs, creating datasets that can be used to improve applications. The app employs LangSmith's features to trace runs, capture feedback, and curate datasets of positive examples, which can then be used to enhance LLM prompts. This process is automated through LangSmith Automations, which will soon be widely available. The initiative not only simplifies dataset construction but also provides practical ways to apply feedback to improve applications. While the current system does not optimize example selection, it lays the groundwork for more advanced optimization using frameworks like DSPy, which focuses on algorithmically optimizing LLM prompts and weights. LangSmith and DSPy share a focus on tracing as a vital component in optimizing LLM systems, allowing for the association of high-level feedback with specific LLM calls. The developers aim to explore further optimization strategies in collaboration with experts like Omar Khattab, who will participate in an upcoming webinar on LLM system optimization.