Model Evaluation
Assess AI model performance systematically. Build evaluation frameworks, design test suites, measure quality metrics, and make data-driven model selection decisions.
What You'll Learn at Each Tier
Write effective single-turn prompts and generate working code for isolated functions. Understand basic AI tool capabilities and limitations.
Apply AI tools across multi-file features. Manage context windows, iterate on outputs, and integrate AI-generated code into existing codebases.
Orchestrate AI across full feature implementations including data layer, API, and tests. Design effective prompt chains and evaluation criteria.
Architect AI-integrated applications with auth, billing, and deployment. Manage AI costs, implement caching strategies, and design fallback patterns.
Design multi-service AI architectures. Coordinate AI across monorepos, implement cross-service AI workflows, and build organization-scale AI strategies.
Sample Challenge
Tier 2 Challenge Preview
Design an evaluation framework for comparing two summarization models. Define 4 metrics, describe how to collect human ratings, specify the minimum sample size for statistical significance, and outline the decision criteria.
Evaluation Criteria
- - Metric selection rationale
- - Statistical rigor
- - Decision framework clarity