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EnglishHow Accurate Are Interviews by AI's Questions and Feedback?
Updated
Interviews by AI generates questions from the job description you paste in (or from curated sample roles), and its feedback is built from three inputs: your job description, the question asked, and your recorded answer. The site itself notes the feedback "isn't perfect." So the honest answer is: accuracy is relative — it scales with how much detail you give it, and it's designed to be a realistic practice aid, not a prediction of the exact questions you'll face.
Where the questions come from
The site describes two sources for generated questions:
- Your pasted job description — the primary input. Paste a real posting and the tool produces behavioral and technical questions tailored to that role.
- Curated sample roles — pre-built sets for common positions like Software Engineer, Business Analyst, Product Manager, Marketing Specialist, and Customer Service Representative, if you don't have a description handy.
The key accuracy lever is stated directly in the FAQ: "the more detail you give, the more tailored they are." A vague one-line description yields generic questions; a full posting with responsibilities, required skills, and team context yields questions that map to the actual role.
What "accurate" means here
The tool isn't claiming to know the exact questions a specific company will ask. It's generating realistic, role-specific questions — the kind that plausibly come up for that position. One user quoted on the site, Henry Tran (Marketing Operations), said a generated question actually came up in his real interview. That's a single data point, not a guarantee, but it illustrates the intended use: broad, plausible coverage rather than exact prediction.
How the feedback is generated
Feedback is derived from three things:
- The job description you provided
- The specific question that was asked
- Your recorded answer (spoken or written)
It's structured around frameworks like STAR (Situation, Task, Action, Result), and each response comes with a stronger sample answer to learn from. This means the feedback is contextual — it evaluates your answer against the role and the question, not against some universal script.
The accuracy caveat
The site is upfront: "It isn't perfect." Treat the feedback as a structured second opinion that catches obvious gaps (missing results, vague actions, no clear situation) rather than an authoritative judgment of whether you'd get the job. It's most useful for spotting patterns across multiple answers.
What makes results more or less accurate
| Factor | More accurate | Less accurate |
|---|---|---|
| Job description detail | Full posting with skills and responsibilities | One-line or vague description |
| Input method | Answering out loud, as in a real interview | Skipping practice or answering in fragments |
| Feedback use | Comparing across several answers to spot patterns | Treating one score as definitive |
| Expectations | Realistic practice coverage | Expecting the exact questions |
Practical takeaway
If you want the most reliable output, paste the complete job description — responsibilities, required qualifications, and any team or product context — rather than a trimmed version. Then practice out loud and read the STAR-based feedback as a coaching signal, not a verdict. The tool is built to make you "walk in already knowing what's likely to come up," and its accuracy tracks how much relevant detail you feed it.