Temperature vs Top-P: Stop Random AI Replies (Complete Guide)

Ever ask the *same* prompt and get *different* answers? That’s sampling at work. This video shows exactly how **Temperature** and **Top-P** shape every AI response—so you can lock in deterministic behavior or dial up creative variety on demand. What you'll learn: ✅ How logits become probabilities (softmax with temperature explained) ✅ When to use Temperature vs Top-P (and why not to crank both at once) ✅ Live demos showing settings from T=0 to T=2 (with production warnings) ✅ Copy-paste presets for coding, writing, and creative tasks ⏰ TIMESTAMPS: 00:00 - Why AI feels inconsistent 00:18 - The hidden controls: Temperature & Top-P 00:37 - Logits 101: Unnormalized scores → probabilities 00:56 - T=0: Greedy/deterministic decoding 01:10 - Higher Temperature = flatter distribution 01:35 - Softmax with Temperature (the actual formula) 02:01 - Live demo: Factual vs creative outputs 02:33 - Top-P (nucleus sampling): Dynamic shortlisting 03:13 - Top-P in action: "The sky is..." 04:06 - Cheat sheet: Best settings by task 04:31 - Next up: **Perplexity (PPL)** — is your base model actually good? 🎯 QUICK REFERENCE: • Coding/Facts: T=0.0–0.2, Top-P=0.8–1.0 (often T alone is enough) • Business Writing: T=0.4–0.7, Top-P=0.9–1.0 • Creative Tasks: T=0.8–1.0, Top-P=0.9–1.0 ⚠️ PRO TIP: Adjust ONE parameter at a time. 🔔 **Subscribe for practical AI insights** - we're breaking down how modern AI actually works, one video at a time. This presentation is inspired by the core concepts in the book "AI Engineering" by Chip Huyen. If you want a deeper dive into these topics, I highly recommend checking it out. 💬 **Questions?** Drop them in the comments - I read and respond to every one. 🎓 Join our FREE AI Engineering Community on Discord: https://discord.gg/rQMxdJJC #AI #LLM #Temperature #TopP #PromptEngineering #MachineLearning #GPT