Building AI Products
Lesson 2 of 6
0%Lesson 2
35 min
LLM Integration Patterns: When to Call the Model and When Not To
Quick Summary
Product decisions about LLMs depend on practical model properties: latency, cost per token, context window, quality at your specific task, and update cadence. Headline benchmarks often do not predict task-specific quality.
What you will learn
- Understand how Large Language Models work at a practical level for product decision-making
- Know the capabilities and limitations of LLMs that affect product design
- Choose the right LLM and API for your product use case
Advanced Content
Sign in to unlock this lesson
LLM Integration Patterns: When to Call the Model and When Not To is part of Building AI Products — an advanced module. We keep these gated so we can save your progress, recommend next steps, and personalize the curriculum.
All beginner content stays free and open — no login required there.