# Product feedback automation
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/auto-feedback.html

> Use Predictive AI models in tandem with Generative AI models to overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text.

# Product feedback automation

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Product%20Innovation%20GenAI/Product%20Innovation%20using%20Generative%20AI%20and%20DataRobot%20Auto%20ML.ipynb)

This accelerator shows how to use Predictive AI models in tandem with Generative AI models and overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text. In a nutshell, it consumes product reviews and ratings and outputs a Design Improvement Report.

The voice of the customer is traditionally captured using feedback from sales channels (which is generally reviews). While consolidating product reviews has been done in a semi-automated fashion with the help of summarization, segmentation, and subject matter expertise, it is always a time-consuming and resource intensive process that spans multiple cycles of rework.

With DataRobot, you can use the generative AI solution framework to build an automated system to process review text and use Generative AI to produce targeted reports for product design and manufacturing teams.

In this accelerator you will:

- Extract high impact review keywords from product reviews using DataRobot.
- Implement guardrails for selecting models with higher AUC to make sure keywords are robust and correlated to the review sentiment.
- Generate product development recommendations for the final report.

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