Скачать или смотреть 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.


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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.

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#AI #LLM #Temperature #TopP #PromptEngineering #MachineLearning #GPT