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A tartalmat a HackerNoon biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a HackerNoon vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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The Metrics Resurrections: Action! Action! Action!

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Manage episode 423218799 series 3474148
A tartalmat a HackerNoon biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a HackerNoon vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/the-metrics-resurrections-action-action-action.
User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #conversational-ai, #ai-applications, #ai-agents, #google-assistant, #user-reported-metrics, #hackernoon-top-story, #user-perceived-metrics, and more.
This story was written by: @pmukherjee. Learn more about this writer by checking @pmukherjee's about page, and for more stories, please visit hackernoon.com.
User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature. However, recent advancements in LLMs allow for the conversion of unstructured user feedback into structured, actionable metrics. This enables teams to better prioritize performance improvement projects by assessing their impact on user perception alongside system-level metrics. While not foolproof, this combined approach provides a more comprehensive understanding of the effectiveness of changes made to conversational AI agents. It's crucial to remember that both types of metrics are valuable for accurately assessing and improving user perception.

  continue reading

238 epizódok

Artwork
iconMegosztás
 
Manage episode 423218799 series 3474148
A tartalmat a HackerNoon biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a HackerNoon vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/the-metrics-resurrections-action-action-action.
User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #conversational-ai, #ai-applications, #ai-agents, #google-assistant, #user-reported-metrics, #hackernoon-top-story, #user-perceived-metrics, and more.
This story was written by: @pmukherjee. Learn more about this writer by checking @pmukherjee's about page, and for more stories, please visit hackernoon.com.
User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature. However, recent advancements in LLMs allow for the conversion of unstructured user feedback into structured, actionable metrics. This enables teams to better prioritize performance improvement projects by assessing their impact on user perception alongside system-level metrics. While not foolproof, this combined approach provides a more comprehensive understanding of the effectiveness of changes made to conversational AI agents. It's crucial to remember that both types of metrics are valuable for accurately assessing and improving user perception.

  continue reading

238 epizódok

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