Core Design Problems Under Interview Conditions
Apply core GenAI concepts in interviews by using them to drive design decisions scaled to the prompt, not by reciting facts. Avoid common pitfalls such as ignoring existing models, safety, or cost, and frame each model pipeline within the surrounding system’s requirements, estimation, and evaluation.
A candidate can explain how diffusion denoises, how Whisper decodes, and how to estimate training time, then spend half the interview on the model and never reach evaluation. The fundamentals are raw material, not the answer. This lesson shows how to turn them into interview moves, and where each one comes back in the lessons that follow.
Say a concept only when it changes a decision
Each fundamentals lesson designed one model pipeline in depth: pick a model, train it, size it, serve it. In an interview, name a concept only when it changes what you build.
Fundamental | Say it when | Skip it when |
Architecture comparison (GAN, VAE, diffusion) | The prompt forces a model choice | The prompt names a model or a hosted API |
Training-time formula | You are asked to train or fine-tune | You are using an existing model |
Inference and token estimate | Sizing serving capacity, which drives cost | Never skip |
Preprocessing and chunking | Data quality or retrieval is the risk | The input is a plain user prompt |
Metrics (WER, FID, MOS, BLEU) | Defining “good” for this task | Listing every metric you know |
Moderation and quality control | The output reaches users | The system is internal and read-only |
A weak answer says, “Diffusion models denoise iteratively.” A strong one says, “I’d use diffusion for prompt fidelity, but 20 to 30 steps keeps latency under a few seconds.” Both mention diffusion, but only the second uses it to make a decision. ...