1️⃣2️⃣ USE DIFFERENT MODELS FOR DIFFERENT JOBS

A real application doesn't need one model for everything. You might use:

• Small model → Classification

• Embedding model → Semantic search

• Vision model → Image analysis

• More capable model → Complex reasoning

• Speech model → Transcription

1️⃣3️⃣ CREATE A MODEL SELECTION CHECKLIST

Before choosing, ask:

• ☑️ What task am I solving?

• ☑️ What quality level do I need?

• ☑️ How much context is required?

• ☑️ What latency is acceptable?

• ☑️ What will it cost?

• ☑️ Does it support the required inputs?

• ☑️ Does it support structured outputs or tools if needed?

• ☑️ What privacy and security requirements apply?

• ☑️ How does it perform on my own test cases?

1️⃣4️⃣ REMEMBER THE MOST IMPORTANT RULE

The best AI model isn't necessarily the most powerful model. It's the model that provides the required quality at an acceptable cost, speed, reliability, and risk level.

🔥 DON'T CHOOSE AI MODELS BY HYPE.

Understand the task, define your requirements, test multiple options, measure results, then decide based on evidence.

💡 Good AI engineering isn't about using the biggest model. It's about using the right model for the right problem.

Double Tap ❤️ For More