Build a local AI interview coach as a repeatable practice loop: choose a question, give a typed or spoken answer, review evidence-based feedback, retry one weak point, and answer a targeted follow-up. Start with text and a clear rubric; add recording and transcription only after the core loop works. A local model can help structure practice, but its feedback is not a measure of hiring readiness.
What the coach should do
A question generator alone does not coach. The useful product is a session that preserves what the user said, explains feedback against the answer itself, and makes it easy to try again. Examples such as DeepInterview and GrillKit illustrate distinct parts of that workflow, including preparation, question tracks, follow-ups, recorded or dictated answers, and review. Their feature sets are implementation examples, not evidence that scores predict interview performance.
- Set up a question. Let the user choose a role, level, or competency, or enter a prompt. Start with a small curated bank; optionally let a local model suggest questions that the user can edit.
- Capture an answer. Accept typed text first. If voice is enabled, show the transcript and let the user correct it before evaluation.
- Evaluate against a rubric. Send the question, answer, and explicit evaluation criteria to the local model. Ask for observations grounded in specific answer evidence.
- Practice one improvement. Offer one focused retry and a relevant follow-up question. Keep the earlier answer and feedback visible for comparison.
- Review and manage sessions. Save the question, answer, rubric version, feedback, and retry history locally by default, explain what is saved, and give users a way to delete sessions.
These are design recommendations, not features guaranteed by either example project. A text-only first version also limits moving parts while you test whether the questions and feedback are useful.
How to connect a local model and make its feedback usable
Call a local inference endpoint
A local model server such as Ollama can provide the language-model layer through its chat API. Keep the endpoint and model identifier configurable so users can select a compatible local setup instead of tying the application to one model.
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Ask for a compact response with predictable fields—for example, an overall observation, evidence-based strengths, one or two improvement points, a non-fabricated suggestion for strengthening the answer, and one practice action or follow-up. Ollama’s API documentation says, “Structured outputs are supported by providing a JSON schema in the format parameter.” That constrains the response format; it does not ensure that a model’s coaching is accurate or insightful. Validate the returned data in application code and show a recoverable error or retry option if it is invalid.
Use a rubric tied to the answer
A practical starting rubric might examine whether the answer addresses the question, has a clear structure, gives specific evidence, distinguishes the candidate’s contribution, explains reasoning or trade-offs, and states an outcome or learning. This is a product-design suggestion, not a validated assessment. Ask the model to point to the answer text supporting each observation, distinguish evidence that is missing from evidence that is weak, and avoid inferring personality or protected characteristics.
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If you display a score, define its levels in the rubric and make it secondary to the evidence and coaching. A 1–5 or 0–100 number can look authoritative without being valid; do not label it as hiring probability or a readiness certification.
Make feedback a conversation
The peer-reviewed Conversate paper describes an interview simulator with adaptive follow-ups, transcript annotations, user reflection, and dialogic feedback that users can discuss and use to refine answers. That design supports a feedback-and-retry loop rather than a one-way score. It does not establish that a particular local model improves employment outcomes.
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How to add spoken practice
Record in the browser
For voice practice, use a browser recording control and check format support rather than assuming every browser accepts the same audio container. MDN documents the MediaRecorder API for recording media streams and producing audio data blobs; its MediaRecorder.isTypeSupported() method can check whether a MIME type is supported in the current browser.
Transcribe after the answer
For a first voice workflow, transcribe the recording after the user finishes, then show the text for correction before sending it for feedback. Batch transcription is simpler than live partial captions and gives users a chance to fix recognition errors that could otherwise distort the evaluation.
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Whisper’s official repository documents file transcription and Python use, and says its command-line tool requires ffmpeg. Its model table gives approximate VRAM requirements: tiny and base about 1 GB, small about 2 GB, medium about 5 GB, large about 10 GB, and turbo about 6 GB. Those are approximate model VRAM figures, not total-system memory requirements. The repository also cautions that speed depends on language, speaking speed, hardware, and other conditions, and that performance varies by language.
Choose a speech model by testing it with the intended languages, speaker, room, and hardware. Compare language support, memory footprint, transcription quality on representative answers, and runtime; the published memory estimates alone cannot select the right checkpoint for a particular machine.
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Do not generalize a single project test
DeepInterview’s maintainers report a 121-second prep and question-plan run on one configuration: Apple M5 Pro with 24 GB, Ollama 0.32.5, qwen3:8b, Speaches/faster-whisper-small, and Kokoro. They say turn latency was not benchmarked, non-English local voice was untested, and other hardware was not verified. This is a project-specific report from 2026, not a general performance expectation. If adding live dialogue, test turn-taking, latency, interruptions, accents, and noisy rooms on the device you intend to support; the cited material establishes no universal latency or accuracy target.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to verify that the app is actually local
“Local” should describe the data path, not just the model’s name. Point language-model and speech-recognition requests to local services, disable hosted fallbacks unless the user opts in, and tell users where recordings and transcripts are stored. DeepInterview’s local-model documentation notes that changing an otherwise similar endpoint to a hosted gateway sends prompts—including CV text, job descriptions, and answers—off-device.
Check more than the main inference request before describing an app as private or offline. Logs, crash reporting, update checks, and configured model endpoints can also affect where data goes. Make the active endpoint visible, and do not silently route private answers to a cloud service if a local server is unavailable.
Handle the common failure cases
- Incorrect transcript: Show the transcript before evaluation and let the user correct it; retain the original answer so a failed transcription does not erase the practice attempt.
- Malformed model output: Validate structured output in the application and offer a clear retry or error state instead of rendering broken feedback.
- Generic or unsupported coaching: Require answer-specific evidence in the rubric and let users retry a focused improvement rather than treating a score as a verdict.
- Context or resource limits: A selected model may have too little context capacity, take too long, or exceed available memory. Keep model choice configurable and report failures clearly.
- Unavailable local service: Preserve the answer and identify the active endpoint. Do not send it to a hosted fallback without the user’s explicit choice.
What this tool can—and cannot—tell a user
Present the coach as practice support. The available interview-practice work describes interaction design and reflective practice; it does not establish a validated mapping from model-generated scores to hiring outcomes or a generally applicable measured learning gain. Keep human judgment relevant, and avoid claims that the tool certifies readiness or predicts whether someone will be hired.
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Choosing models without a misleading leaderboard
The cited documentation and projects identify trade-offs, but do not provide a complete cross-model benchmark for this use case. Test candidate language models on representative questions and answers, comparing rubric adherence, evidence-grounded feedback, structured-output reliability, context capacity, latency, memory use, and local licensing or redistribution terms. For speech recognition, use the criteria above and test on the target speaker and room rather than relying on a model label or approximate VRAM figure alone.
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