This guide provides an extensive overview of sampling techniques employed in Large Language Models (LLMs) to generate diverse and coherent text. It begins by explaining why LLMs utilize sub-word "tokens" instead of individual letters or whole words, detailing the advantages of this tokenization approach. The core of the document then introduces and technically explains numerous sampling methods like Temperature, Top-K, Top-P, and various penalties, which introduce controlled randomness into token selection to avoid repetitive outputs. Finally, the guide examines the critical impact of sampler order in the generation pipeline and expands on the intricacies of tokenizers, illustrating how their design fundamentally influences the LLM's output.