What I Learned Shipping AlgoMentor
Sujal Vijayvargiya
SDE-1
Building a production AI mock interview platform taught me that structured LLM outputs, TTS latency buffering, and adaptive question generation require completely different engineering instincts than typical CRUD apps.
The Prompt Engineering Trap
My first iteration of AlgoMentor used free-form prompts asking the LLM to 'evaluate this DSA solution'. Results were inconsistent — the same solution would score differently on retries, making the feedback meaningless.
The fix was structured output schemas enforced via Groq's constrained JSON generation mode. Every evaluation response now has a deterministic shape: correctness score, time complexity assessment, communication clarity score, and a concrete improvement suggestion. No more hallucinated scoring.
TTS Latency Is a UX Problem
AWS Polly's neural voices are high quality, but synthesis latency for longer utterances made the interviewer feel unnatural. The solution was streaming synthesis and chunking questions into sentence-level audio segments, pre-generating the next segment while the current one plays.
The result is a near-zero perceived gap between the interviewer 'thinking' and speaking — which meaningfully improves immersion.
Adaptive Questioning Is a Retrieval Problem
The most valuable feature of a real interviewer is adjusting difficulty based on candidate performance. Building this required classifying each candidate response in real-time and using that signal to select the next question from a structured problem bank.
Llama-3.3-70B via Groq handles the classification and generates the follow-up prompt — the low inference latency was the key enabler here.
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