# Customer communication AI

> Customer communication AI - Learn how generative AI models like GPT-3 can be used to augment
> predictions and provide customer friendly subject matter expert responses.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.578281+00:00` (UTC).

## Primary page

- [Customer communication AI](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/comm-genai.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [LLM and GenAI applications](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/customer_communication_datarobot_gen_ai/effective_customer_communication_datarobot_gen_ai.ipynb)

In this accelerator, you will see how you can integrate LLM-based agents like ChatGPT with DataRobot Prediction Explanations to quickly implement effective customer communication in AI-based workflows.

In banking and fintech, one of the most critical communication provided to the customer is refusal of products and services, like loan application rejection. When a machine learning model predicts high loan default probability, organizations need to relay the rejection to the applicant in an effective way to sustain customer satisfaction, avoid churn, and not reduce the customer lifetime value. Effective communication also needs subject matter expertise, which becomes costly if implemented at the granularity of every application.

DataRobot’s Prediction Explanations provide the context for prediction and the LLM agent provides the subject matter expertise to provide effective yet positive responses to adverse event predictions. This allows organizations to effectively communicate their AI-based decisions with their customers while improving costs and productivity related to their expert personnel.