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Choose the Right AI Model for Your AI Agent

🎥 ✅ 🔥 FREE Youtube Course to transitioning from a UX Designer → Design Engineer in the AI era. :    • UX Designer to Design Engineer in AI era   20B vs 70B parameters — what do they actually mean, and does a bigger model always perform better? In this episode, Nickey asks the question every designer has, and Jackey breaks it down with simple analogies: parameters = “knobs” the model learned during training. If you’re building AI products, this video helps you choose the right model for your use case—without wasting cost, adding latency, or making the experience harder to control. What you’ll learn (fast + practical) What parameters mean (20B vs 70B vs 400B+) Why bigger models can be slower, costlier, harder to control How to pick the right model size based on your use case Why prompt quality can beat a “bigger model” What weights & biases are (and why they affect outputs) What designers do in real products:✅ system prompts✅ guardrails✅ evals(instead of tuning weights) 💡 Key idea: You’re not just choosing a model — you’re choosing how much intelligence your experience needs. 📌 LINKS ▶️ Main Channel: / @designwithdonkeys 📸 Instagram: instagram.com/designwithdonkeys 𝕏 Twitter : x.com/designwithdnkys ⏱ Timestamps 0:00 Intro 0:17 What does “20B / 70B parameters” mean? 1:20 Model size depends fully on your use case 4:30 Prompt quality matters more than people think 8:08 What are weights and biases in AI models? 9:18 Key takeaways for a design engineer #ai #llm #productdesign #uxdesign