Illustrative scenario
LLM engineering: make code and model reasoning testable
The gap
The resume lists models and frameworks but answers omit data splits, baselines, metrics and failure analysis. Personal implementation is difficult to distinguish from team infrastructure.
Target job requirements
Write Python, design retrieval and evaluation workflows, analyze model and data problems, and reason about quality, latency and cost.
Repeated follow-up questions
- Write pseudocode to deduplicate retrieval results and explain complexity and tests.
- How would you separate retrieval failure from generation failure?
- How can data leakage distort offline evaluation, and how would you prevent it?
- How would you assess a quality gain that substantially increases latency?
Focused practice
Build a baseline and evaluation set from the actual project. Practice readable code, edge cases and failure categories, and describe personal experimental and implementation evidence.
What changes in this example
The scenario connects code, data, metrics and tradeoffs for deeper technical discussion. Code is reviewed by AI and is not executed on the server.
Practice this role