2.1. Selecting and Configuring Foundation Models

💡 First Principle: Foundation model selection is an engineering decision, not a marketing decision. The correct model is the one that meets the minimum quality threshold for your task at the lowest cost — not the largest, newest, or most well-known model.

Getting selection wrong has compounding consequences: an oversized model increases costs 10–100x for no measurable quality gain; an undersized model produces outputs that fail quality thresholds and require expensive post-processing or human review. The exam tests whether you can read a scenario's constraints — latency budget, cost ceiling, task type, context window requirement — and select accordingly.

The business impact of poor model selection is immediate and visible: a GPT-scale model running 10 million customer support queries per month at $0.015 per 1K tokens costs $150,000+. A smaller fine-tuned model meeting the same quality bar at $0.0008 per 1K tokens costs $8,000. Selection is a cost architecture decision.

⚠️ Common Misconception: A larger foundation model always produces better results for your use case. Smaller, fine-tuned or instruction-tuned models routinely outperform general large models on narrow domain-specific tasks. Always benchmark against your actual task before committing to a model.