A useful interview coach should remember more than the last answer. It should connect what happened in one interview to what the candidate needs to do next—and still know the difference between a transcript, a personal preference, and a commitment with a deadline.
That distinction became the central design problem in RoundReady. I built it as a Flask application that captures interview conversations, extracts structured feedback, stores useful context in Hindsight, and uses that context to generate a company- and role-specific preparation roadmap.
RoundReady supports several ways to bring an interview into the system: a pasted transcript, an uploaded audio recording, or audio captured from a shared browser tab. In the live flow, the browser sends completed audio chunks to the server for transcription. The user can also review and save a conversation rather than treating the live stream as a permanent record by default.
After transcription, the backend asks a language model to extract a structured set of observations: questions and answers, weak points, quality scores, interviewer style, explicit promises, and next steps. The point of that structure is not to turn every response into a score. The extraction prompt tells the model not to invent missing details, and to leave scores empty when there is not enough evidence.
Source: DEV - interview — Read the full article on the original site ↗