What this prompt does
This prompt assembles an interview research brief for a specific company. You give the [company_name], [position], and [location], and the AI compiles ten sections: a company overview with recent [news_period] news, engineering culture signals, the likely tech stack and architecture, the interview process across [expected_rounds], commonly reported questions, team structure, likely team challenges, smart questions to ask them, red flags to watch for, and a salary-range estimate — capped with a one-page cheat sheet for last-minute review.
The structure works because interview prep is really intelligence gathering, and scattered Glassdoor threads, blog posts, and job listings only become an edge when synthesized. By organizing those signals into culture, stack, process, and questions, the prompt turns research into something you can act on. The [position] and [expected_rounds] variables focus the brief on the role you're actually interviewing for rather than the company in general.
When to use it
- You have an interview at
[company_name]and want a focused prep brief, not scattered tabs. - You want to understand the likely tech stack and architecture before technical rounds.
- You need to know what the
[expected_rounds]typically involve. - You want thoughtful questions to ask that signal genuine technical interest.
- You want a one-page cheat sheet to review 30 minutes before the interview.
- You want a salary-range estimate for
[location]to anchor negotiation.
Example output
Expect a ten-section brief: company overview with recent news, engineering-culture notes, an inferred tech stack, a breakdown of the [expected_rounds], commonly reported questions, team-structure guesses, likely challenges, questions to ask, red flags, and a [location] salary estimate — distilled into a one-page cheat sheet at the end.
Pro tips
- Treat the recent-news and salary sections as starting points to verify; the model's knowledge has a cutoff, so confirm anything time-sensitive against current sources.
- Set
[position]precisely — a backend role and a frontend role at the same company face different rounds, and accuracy sharpens the whole brief. - Use the suggested questions to ask as inspiration, then tailor a few to something specific you noticed about the company, which lands far better than generic prompts.
- Cross-check the inferred tech stack against the actual job listing, since the model infers from patterns and the real posting is authoritative.
- Lean on the one-page cheat sheet for final review, but build genuine familiarity earlier — a cheat sheet skimmed cold won't substitute for real preparation.
- Read the red-flags section as a checklist of things to actively probe during the interview, not just passively note, since the conversation is your best chance to confirm them.
- Verify the
[location]salary estimate against a current source before using it in negotiation, as compensation data shifts and the estimate is directional.
