Google AI interview practice: tools and limits

Two Google products are genuinely useful for interview practice, and both are free.

Interview Warmup is a browser tool from Google's career-skills initiative. You pick a field, answer common interview questions by voice or text, and it transcribes what you said and highlights patterns: the job-related terms you used, the points you repeated, and the shape of your answers. It is a mirror rather than a judge; it shows you what you said without scoring it.

Gemini is the general assistant, and with a clear prompt it becomes a serviceable role-play partner: paste the job description, ask it to interview you for the role one question at a time, and request a critique after each answer. It follows the role context and can go deeper on request.

Neither requires payment, which makes them the right first stop. The honest question is not whether they are worth using, but where they stop, and that is the next two sections.

Warmup earns its place in a preparation plan for one reason: friction-free reps. Opening a browser and answering five questions out loud is the lowest-cost way to hear your own filler words, notice answers that trail off, and get comfortable speaking to a silent screen, which is most of what a video interview feels like.

Its limits are structural, not bugs:

• Generic question banks. Questions come from the chosen field, not from a specific job description or your resume, so the reps stay general.
• Patterns, not judgment. Highlighting job-related terms tells you what you covered; it does not tell you whether the answer would satisfy an interviewer.
• No follow-ups. Real interviews probe. Warmup asks the next question regardless of what you said, so the pressure of a probing second question never gets rehearsed.

Use it early, when the goal is fluency rather than precision, and move to something role-specific once the basics feel boring.

You want practice that matches the actual job, and a generic question bank cannot give you that. This is where Gemini does real work. The short version: give it the job description, force one question at a time, and make it push back.

A prompt shape that works: paste the posting, then ask it to act as the hiring manager, ask one question, wait for your answer, then ask one probing follow-up before moving on, and end each exchange with a two-line critique. Without those constraints it tends to dump question lists or accept weak answers agreeably, and agreeable practice builds false confidence.

A marketing analyst preparing for a rotational-program interview ran Warmup for a week to burn off filler words, then switched to Gemini with the posting pasted in and drilled the three motivation questions the program is known for. The mix worked because each tool did the part it is built for: one gave reps, the other gave role context.

Speak your answers aloud even when the tool is text-first; typed fluency does not transfer to a live call. The mock interviews cluster collects drills for making practice transfer.

The gap the free layers leave is the full session: role-specific questions in sequence, follow-up pressure, and a review at the end that tells you what to fix before the next run. That is the specific job of a dedicated mock interview tool; SubcueAI's mock mode generates role-specific questions and produces a post-session review, which closes the loop the free tools leave open.

The honest framing: Google's free layers are genuinely useful and cover a large share of preparation on their own. A paid mock adds structure, not magic; it earns its place when the interview matters enough that unrehearsed follow-ups are the risk you want to remove.

One reading of this query deserves a direct answer too: if you are preparing for an interview at Google itself, the same practice logic applies, and nothing here changes the ethics of the real conversation. Practice is where AI belongs without caveats; live use has honest limits that depend on the scenario, and those are documented rather than hand-waved across this library.