OpenAI AI Engineer Interview Questions

I would store only the parts of history that are actually useful for future interactions, and I would separate short-term context from long-term memory. Memory should be selective, user-controlled, and safe, because keeping everything creates privacy and relevance problems. A good system remembers what matters without becoming noisy or invasive.

I would treat retrieved or user-provided content as data, not instructions. That means isolating it from the system prompt, validating tool calls, and ignoring any instructions that come from untrusted text. The safest system assumes hostile input is normal, not exceptional.

I would compare the prompt template, model version, retrieval configuration, and evaluation traffic before and after the change. Then I would reproduce the issue on a fixed test set so I can isolate whether the regression is from the model, the data, or the application layer. Strong debugging in AI means treating the whole stack as the product.

I would reduce unnecessary tokens, cache repeated work, batch requests where possible, and use smaller models for simpler subtasks. I would also check whether every step really needs the full model or whether some logic can be handled deterministically. Cost and latency usually improve together when the pipeline is simplified.

I would start with clear instructions, structured output formats, and concrete examples of the desired behavior. Then I would test against difficult cases and refine based on failures rather than style alone. Prompt engineering works best when it is treated as an empirical process.