Valenx

Posted on Nov 11

AI-assisted

What the interviewer is testing

Whether you can distinguish prompt-based learning from weight updates, compare their costs and deployment needs, and choose retrieval for frequently changing facts.

Answer framework

In-context learning puts instructions and examples into the prompt without changing the model weights.

In-context learning supports immediate iteration, but each request pays for the extra prompt tokens and behavior remains bounded by the context window.

Fine-tuning trains on examples and updates model parameters, potentially making formats, tone or narrow-task behavior more consistent.

Fine-tuning can make a smaller model perform as well as a larger prompted model, but it requires data, training, evaluation and redeployment.

Use retrieval rather than fine-tuning for facts that change frequently, because retrieval lets the source of truth be updated without retraining.

Sample answer

I would start by separating what changes.…