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Developer uses state machine to control LLM in AI interviewer

By PulseAugur Editorial · [1 sources] ·

A developer found that relying solely on a large language model (LLM) for an AI technical interviewer led to inconsistent and unreliable performance, with the model frequently ending interviews prematurely or repeating questions. To address this, the developer implemented a finite state machine to manage the interview's structure and flow, reserving the LLM's role for specific tasks within each state, such as generating questions or hints. This approach improved reliability and, counterintuitively, allowed for more natural LLM responses by providing a stable framework. AI

IMPACT This approach offers a robust method for controlling LLM behavior in applications requiring predictable workflows.

RANK_REASON Developer describes a practical application of LLMs within a structured system.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer uses state machine to control LLM in AI interviewer

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19 / 100
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Tool
Developer describes a practical application of LLMs within a structured system.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, other
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Valipireddy Kowshik ·

    Why I Ditched "Just Let the LLM Handle It" for a State Machine (And Slept Better at Night)

    <p>When I started building my AI technical interviewer, I did what most people do: I threw a big system prompt at the LLM and told it to "act like an interviewer, ask coding questions, give hints when the candidate is stuck, and score them at the end."</p> <p>It worked... about 8…

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