While building an AI-powered product, I quickly realized that adding an AI feature isn't just a question of whether it works-
that's also the question of whether it makes sense to run it every time.
some AI features can genuinely improve the experience, but using AI too frequently can make the product unnecessarily expensive, especially when you're still building and don't have predictable usage.
For other makers building AI products, how have you balanced useful AI functionality with API costs?
I've been building an AI-powered Interview and career practice platform(Ultimate AI Interviewer) designed to help people practice interviews, improve communication, and understand where they need to grow.
what started as an interview-practice idea gradually became a much broader career platform.
Along the way, I ended up Building:
AI Powered interview practice with an interactive interviewer for corporate roles, competitive exams, higher education, and government-related opportunities.(STAR method evaluation, Non- communication and communication analysis).
Coding practice and evaluation.
Hiring readiness and performance indicators.
Resume and career-oriented features.
Live Practice and Mentor Interactions.
Social and community features for career growth.
An AI assistant that adapts to different use cases based on what the user needs- from interview guidance and mentorship to general conversations.
