Uploaded April 2026 | Updated September 2026, 3 weeks ago
The development of an Ollama AI Judge Assertion plugin for JMeter. Building on previous work involving the Ollama stream sampler, the presenter demonstrates how to implement an automated assertion that uses an AI model to evaluate test responses as either pass or fail based on specific criteria (0:00-1:06).
GitHub Repo: github.com/QAInsights/jmeter-plugin-development-series
Key Implementation Highlights:
Conceptual Logic: The plugin functions like a judge, taking an input (e.g., a prompt) and using an AI model to perform tasks like fact-checking or validation (1:10-2:25).
Technical Walkthrough:
The developer explains the GUI structure, extending the AbstractTestElement and implementing the Assertion interface (3:16-3:35).
The core logic involves extracting data from the sampler and passing it to the wamaJudge.judgeResponse method, which sends the evaluation prompt to the AI (3:36-4:25).
Demonstration in JMeter:
The presenter shows the plugin in action by testing a positive scenario ("The sun rises in the east"), which returns a pass (4:46-5:16).
A negative scenario ("1 + 1 = 3") demonstrates how the judge assertion correctly identifies the inaccuracy and returns a fail (5:17-5:41).
The video concludes by suggesting that this approach allows for flexible evaluation frameworks, such as using different models for comparison or cross-model validation within JMeter (5:42-6:24).
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The development of an Ollama AI Judge Assertion plugin for JMeter. Building on previous work involving the Ollama stream sampler, the presenter demonstrates how to implement an automated assertion that uses an AI model to evaluate test responses as either pass or fail based on specific criteria (0:00-1:06).
GitHub Repo: github.com/QAInsights/jmeter-plugin-development-series
Key Implementation Highlights:
Conceptual Logic: The plugin functions like a judge, taking an input (e.g., a prompt) and using an AI model to perform tasks like fact-checking or validation (1:10-2:25).
Technical Walkthrough:
The developer explains the GUI structure, extending the AbstractTestElement and implementing the Assertion interface (3:16-3:35).
The core logic involves extracting data from the sampler and passing it to the wamaJudge.judgeResponse method, which sends the evaluation prompt to the AI (3:36-4:25).
Demonstration in JMeter:
The presenter shows the plugin in action by testing a positive scenario ("The sun rises in the east"), which returns a pass (4:46-5:16).
A negative scenario ("1 + 1 = 3") demonstrates how the judge assertion correctly identifies the inaccuracy and returns a fail (5:17-5:41).
The video concludes by suggesting that this approach allows for flexible evaluation frameworks, such as using different models for comparison or cross-model validation within JMeter (5:42-6:24).
➡️ Subscribe to my blog qainsights.com
➡️ Join QAInsights Community at https://qain.si/community
➡️ Join QAInsights Academy at https://qain.si/academy
➡️ Buy me a tea 🍵 buymeacoffee.com/qainsights
➡️ Get Certified in CKAD https://qain.si/startk8s
➡️ Get Certified in CKA https://qain.si/cka
➡️ My preferred DNS is NextDNS https://qain.si/nextdns
➡️ Learn Linux https://qain.si/linux
➡️ Get performance testing jobs real quick using Indeed → goo.gl/XAfCcE
➡️ Hostinger Web Hosting → goo.gl/MfwDyU
➡️ My Productivity Tools → goo.gl/2DfC5d
➡️ App Sumo for your business → goo.gl/zj92SA
➡️ Amazon → amzn.to/2L0Jv2n
➡️ TubeBuddy → tubebuddy.com/qainsights
➡️ LoadRunner Playlist youtube.com/playlist?list=PLJ9A48W0kpRIiVf8W7jMvf6Ao-naX3Ari
➡️ My first Udemy course entitled `Performance Testing using DevWeb` has been published https://qain.si/devweb
