Private AI for technical interview question banks
A technical interview question bank is only useful while it's unknown to candidates. Generating it with a general-purpose AI tool works fine the first time; the harder question is what happens to the copy of that bank sitting in a vendor's logs afterward, and whether the same prompts get reused by other engineering teams asking a similar model for similar questions.
Why interview questions leak faster than most internal documents
Question banks circulate more widely than most people expect: candidates share them on interview-prep forums, engineers post them to private group chats after a loop, and some candidates paste questions into their own AI tools mid-interview to get an answer. None of that is new to AI-assisted hiring. What's specific to AI-drafted question banks is the extra copy created at generation time, sitting on infrastructure the hiring team doesn't control, under a retention policy nobody on the hiring team reviewed.
There's also a subtler risk with shared, general-purpose models: a phrasing or a specific coding problem generated for one company's loop can resemble what the same model generates for a different company asking a similar prompt, simply because both requests draw from overlapping patterns the model has seen. That's not a data breach in the traditional sense, but it does mean a hiring team asking a shared service for "a medium-difficulty graph problem for a backend interview" isn't necessarily getting something unique to their own process.
What changes when the generating model is self-hosted
Running the question-generation assistant on a dedicated DGX Spark keeps the prompts, the drafts, and the final bank inside the hiring team's own environment, with no vendor-side log of what was asked or generated. The recruiting team still gets a model that can draft a set of coding problems at a target difficulty, or generate variations on an existing question so it can't be pattern-matched against a public writeup; none of that material leaves to get generated.
Open WebUI restricted to the hiring managers and recruiters who need it is a practical way to run this, with prior loops and rubrics kept as reference material similar to the approach in our piece on on-premise RAG, so new questions can be checked against what's already been used without that history sitting on anyone else's infrastructure. Teams evaluating candidates on AI-assisted coding workflows may also want to see our note on private AI code review, since the same self-hosting logic applies to reviewing candidate take-home submissions.
What the model is useful for, and what stays with the interviewer
A generation model is a reasonable help for drafting question variations, checking a rubric against a candidate's written answer, and keeping a bank of questions organized by difficulty and topic. The hiring decision itself, and any judgment call on a borderline or unconventional answer, should stay with the human interviewer. That's partly a fairness question and partly a legal one: several jurisdictions already restrict how much weight an automated system can carry in an employment decision.
What this doesn't solve
Self-hosting the generation model doesn't stop candidates from sharing questions with each other after the fact, and it doesn't replace a hiring team's own process for rotating stale questions out of the bank. What it removes is the specific exposure of a vendor holding a copy of your question bank before you've decided who gets asked what. See pricing for what a dedicated Spark costs for a recruiting or engineering team.