Behind 43 offline tests is a clear judgment: the quality of an AI interviewer depends less on how smart the model is than on whether its persona can be translated into executable, verifiable working rules. A prompt that merely says “act like a real interviewer” may still collapse into polite small talk; explicitly banning encouraging phrases and requiring the model to identify mistakes after incorrect answers produces more consistent results.
What This Is
This is a development method for simulated-interview agents. An agent—a program that can invoke a model and complete tasks according to a workflow—is divided into four layers: identity determines the interview role and candidate level; rules specify what to assess and how to follow up; boundaries restrict the agent to asking one question at a time; and style uses a specific vocabulary to ban customer-service phrases such as “No problem” and “Keep going.”
Prompting also plays an engineering role here: the system prompt remains fixed, while user input contains only the candidate’s answer. Job requirements and difficulty are handled as independent data. Templates define “how to ask,” configurations define “what to ask,” and prohibitions define “how not to phrase things,” making it easier to switch roles and run tests.
Industry View
We have noticed that projects like this move large-model applications from “tuning outputs” to “managing constraints.” Many earlier products relied on a one-line persona and a handful of examples, with output instability often blamed on the model. This layered approach at least breaks the problem into engineering stages that can be isolated and regression-tested.
The risks are equally clear: the quality of follow-up questions, the fairness of scoring, and job coverage still depend on training data and model capabilities. Eliminating customer-service language does not confer genuine hiring judgment. Without human review, an AI interviewer may simply shift from being excessively encouraging to being excessively harsh—or assess candidates using incorrect knowledge. The 43 offline tests demonstrate that the rules are being enforced, not that the interview itself is effective.
Impact on Regular People
For enterprise IT: When enterprises deploy AI interview and training assistants, the priority is not merely connecting a model, but building a system of prompts, data configurations, and tests. If job knowledge is inaccurate, the system will consistently repeat those errors.
For individual professionals: These tools are better suited to low-cost practice with follow-up questions and verbal expression. Their conclusions should not be treated as hiring scores. In real use, candidates should still compare them with the target role’s examination syllabus and ask experienced people to review key feedback.
For the consumer market: The selling point of AI job-search products will shift from “having someone to chat with” to “whether it can sustain role-specific follow-up questions.” Users will also demand greater transparency, including the ability to inspect assessment points, the basis for identifying errors, and the reasons behind scores.