Explore how LLMs choose words. We break down Greedy, Beam, Top-k, and Nucleus sampling strategies to help you optimize AI output for speed, creativity, and accuracy.
Token probability distributions determine how language models choose the next word. Learn how softmax, temperature, top-k, and top-p sampling shape AI-generated text - and why understanding them gives you real control over AI behavior.
Learn how sampling methods like temperature, top-k, and nucleus sampling directly impact LLM hallucinations. Discover the settings that reduce factual errors by up to 37% and how to apply them in real-world applications.