OpenAI AI Engineer Interview Questions

I would start with the user problem, define success metrics, and compare the feature against a baseline or current workflow. A good answer should show that I care about real user impact, not just model quality in isolation. I would also include qualitative feedback because metrics alone can miss whether the feature feels helpful in practice.

I would combine better prompting, retrieval from trusted sources, constrained generation, and validation checks on the output. The goal is not to assume one technique will solve everything, but to reduce the chance of unsupported claims across the whole system. I would also test on failure cases that reflect real user behavior rather than only clean benchmark data.

Prompting changes the input instructions, fine-tuning changes the model weights, and retrieval-augmented generation adds external knowledge at inference time. I would use prompting first, retrieval when the model needs current or domain-specific facts, and fine-tuning when I need consistent behavior that prompting cannot achieve. The right choice depends on cost, latency, control, and maintainability.

I would ingest the documents into a searchable index, retrieve the most relevant chunks, and ask the model to answer only from that context. I would also add citations, permissions filtering, and fallback behavior when the system is unsure. That makes the assistant more trustworthy and easier to audit.

I would break latency into model time, retrieval time, preprocessing, and postprocessing so I know where the bottleneck is. Then I would reduce the slowest parts with caching, smaller models, fewer tokens, or better batching. Good latency work is about making the user experience feel responsive without sacrificing quality.