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Developer tracks chatbot performance using golden set and gap analysis

A developer is evaluating a chatbot designed to answer questions about pasture growth rates for rural workers. The chatbot's performance is measured against a 'golden set' of 68 questions, with a separate agent auditing the answers for missing information or knowledge gaps. In a baseline test on September 14, only one answer passed outright, with 35 rejected and 205 identified gaps. The developer hypothesized that fixes would reduce rejected answers and improve data accuracy, but the subsequent audit on September 15 was cut short due to API credit depletion, leaving the chatbot's current performance unmeasured. AI

IMPACT Provides insight into the practical challenges and methodologies of evaluating chatbot performance and identifying specific areas for improvement.

RANK_REASON Developer's personal blog post detailing a specific, non-generalized evaluation process for a chatbot.

Read on dev.to — LLM tag →

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

Developer tracks chatbot performance using golden set and gap analysis

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Developer's personal blog post detailing a specific, non-generalized evaluation process for a chatbot.
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  1. dev.to — LLM tag TIER_1 English(EN) · Lisandro Reinoso ·

    Questions for a chatbot

    <p>Today I have a file open with an empty table. At the top are the numbers from two weeks ago: out of 68 answers, one passed. At the bottom, the hypotheses I wrote so I wouldn't cheat myself when measuring again. The table in the middle, the one that would say whether the chatbo…