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1. ⚙️ Voice Input Compatibility (Web Speech API)

• Problem: Not consistently supported across browsers (e.g., Safari).
• Impact: Voice-based features might break or behave differently.
• Fix: Use a fallback or switch to a more reliable service (e.g., Google Speech-to-Text).

2. 🧠 NLP/AI Analysis Accuracy

• Problem: Feedback quality depends on the model’s ability to understand user input.
• Impact: Inaccurate or vague feedback can frustrate users.
• Fix: Use fine-tuned models or combine LLMs (like GPT) with domain-specific rules.

3. ⚡ Latency from External APIs

• Problem: Speech-to-text and LLM APIs introduce latency.
• Impact: Slower feedback = bad UX, especially in real-time simulations.
• Fix: Preload questions, cache results, or use batch processing.

4. 🧱 Complexity of Adaptive Logic

• Problem: Dynamically adjusting question flow adds algorithmic and UI complexity.
• Impact: Hard to maintain or debug.
• Fix: Design modular, testable logic trees; start simple and iterate.

5. 🔒 Privacy with Voice & Answer Data

• Problem: Voice and interview answers are sensitive user data.
• Impact: Legal compliance risk (GDPR, CCPA).
• Fix: Encrypt storage, anonymize logs, and disclose usage clearly in TOS.

6. 📱 Mobile Experience May Suffer

• Problem: Voice input, modals, and rich feedback can be clunky on mobile.
• Impact: Poor adoption among mobile users.
• Fix: Mobile-first design, test across screen sizes.

7. 📉 Cost Scaling with API Usage

• Problem: APIs like OpenAI or Google Cloud Speech get expensive at scale.
• Impact: High server costs with lots of users or long interviews.
• Fix: Monitor usage, optimize requests, or switch to cheaper or self-hosted alternatives.