This repository aggregates academic papers and resources on LLM prompt tuning and automatic optimization techniques published after 2022. It organizes content across multiple categories including fine-tuning methods, reinforcement learning approaches, gradient-free optimization, in-context learning, and Bayesian optimization for prompts. The collection serves as a reference bibliography for researchers working on improving LLM performance through prompt engineering.