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![[On-Device AI Chatbot] Part 8: Catching Hallucinations: Analyzing SuperVision Test Results](https://static.wixstatic.com/media/2ea07e_69fba1e933354148a97a50bbfb2f2dcb~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_35,blur_30,enc_avif,quality_auto/2ea07e_69fba1e933354148a97a50bbfb2f2dcb~mv2.png)
![[On-Device AI Chatbot] Part 8: Catching Hallucinations: Analyzing SuperVision Test Results](https://static.wixstatic.com/media/2ea07e_69fba1e933354148a97a50bbfb2f2dcb~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_95,enc_avif,quality_auto/2ea07e_69fba1e933354148a97a50bbfb2f2dcb~mv2.png)
[On-Device AI Chatbot] Part 8: Catching Hallucinations: Analyzing SuperVision Test Results
Catching Hallucinations Analyzing SuperVision Test Results In Part 7, we built an automated testing pipeline that bridged our on-device chatbot app inside a smartphone with the AI SuperVision server on a PC. This enabled an end-to-end flow from prompt injection and answer extraction to automated grading. We finally had an environment capable of running dozens of test cases automatically. So, what kind of report card did our on-device SLM (Gemma-2B based) receive from these
1 day ago
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