Explores how to control the text generated by Large Language Models (LLMs) by examining various decoding strategies and sampling parameters. Key parameters like temperature, top-k sampling, and top-p (nucleus) sampling are explained, detailing their mechanisms and impact on balancing output creativity versus coherence.

Also discusses the history and evolution of these techniques, highlighting newer, more adaptive methods and the importance of practical experimentation for task-specific tuning. Finally, it touches upon additional user-defined constraints that further shape LLM outputs.