Pure prompt-engineer roles are folding into AI product engineering, but the interview questions persist: companies test systematic prompting, evaluation discipline, and product judgment around LLM behavior. Here's what gets asked.
What a strong answer covers: Three things: an eval set (real examples with pass criteria) so changes are measured not vibed, version control treating prompts as production code, and isolating one variable per change. Techniques matter less than the loop. A candidate who says 'I never change a prompt without running the eval suite' has answered the real question.
What a strong answer covers: Cover with judgment, not a list: clear role/task framing (always), few-shot examples selected from real failure cases (when format or edge-case behavior drifts), chain-of-thought or 'think before answering' (reasoning tasks; note reasoning models change this), structured output schemas (anything parsed downstream), decomposition into chained prompts (when one prompt does too many jobs), and negative examples (persistent failure patterns). Knowing when a technique is unnecessary is the senior signal.
What a strong answer covers: Diagnose systematically: production inputs differ from test inputs (longer context burying instructions, adversarial or messy user text), context assembly bugs (retrieved content pushing instructions out or contradicting them), conversation history accumulating instructions that conflict, and instruction placement (models weight beginnings and ends of context). Reproduce with the exact failing production payload before changing anything — the fix is usually in assembly, not wording.
What a strong answer covers: Layered, honest answer: separate trusted instructions from untrusted content structurally (system vs user roles, delimiters — helpful but not sufficient), treat all retrieved/user content as data not instructions, validate outputs against expected schemas, restrict tool permissions so a hijacked model can't do damage (least privilege is the real defense), and monitor for anomalous outputs. State plainly that no prompt-level defense is complete — architecture carries the safety weight.
What a strong answer covers: The 'never' is the trap — prompts alone can't guarantee never. Answer with defense in depth: explicit policy in the system prompt with examples, retrieval of actual policy documents, output classification catching refund promises before sending, tool-gated actions (the bot can't issue refunds, only create tickets), and escalation paths. Interviewers want to hear 'the guarantee lives in the system architecture, the prompt reduces frequency.'
What a strong answer covers: Turn subjective into measurable: define a rubric with the stakeholder (what does friendly mean — greeting, tone markers, absence of jargon?), collect human ratings on a sample to establish baseline, calibrate an LLM judge against those human ratings, then use pairwise comparisons (easier to judge than absolute scores). Report agreement stats, not just scores. This question tests whether you can operationalize fuzzy requirements.
What a strong answer covers: Split when: one prompt juggles multiple jobs with degrading quality, intermediate outputs need validation, or different steps want different models. Costs: added latency per hop, error propagation between steps, more surface to maintain, and harder end-to-end evaluation. The senior answer includes the reverse: newer models handle more in one shot, so periodically re-test whether the chain is still necessary.
What a strong answer covers: Take it seriously — humans detect real regressions before coarse metrics do. Steps: collect the specific examples behind the feeling, check for provider model updates, prompt or context-assembly changes, and input-mix shifts; run the examples through the eval suite and add them if they expose a gap your set missed. The meta-answer: 'feels dumber' reports usually mean your eval coverage has a hole.
What a strong answer covers: Models differ in instruction-following style, format preferences, and verbosity defaults; reasoning models invert some habits (explicit chain-of-thought prompting can hurt). Management: per-model prompt variants behind one interface, eval suites run per model, migration checklists for version bumps, and resisting per-model tweaks until evals prove they're needed. The principle: portability through evals, not through hoping one prompt fits all.
What a strong answer covers: Method over cleverness: identify what's actually load-bearing by ablation (remove sections, run evals), convert prose rules to terse structured lists, move rarely-needed instructions to conditional injection (only include refund policy when the query is about refunds), replace verbose examples with minimal ones covering the same failure modes, and re-run the full eval after each cut. Typical honest result: half the tokens with no measurable quality loss.
What a strong answer covers: Honest framing wins: pure prompt tuning is commoditizing, but the surrounding skills compound — evaluation design, LLM product judgment, failure-mode analysis, and translating product requirements into model behavior. Position yourself as an AI product engineer whose prompting is one tool. Companies asking this want someone who'll grow with the field, not defend a shrinking niche.
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