---
title: Generative AI accelerators
description: Workflows that integrate generative and predictive AI.

---

# Generative AI accelerators

Topic | Describes... |
----- | ------ |
[Anti-money laundering (AML) alert scoring](alert-scoring) | Develop a machine learning model that utilizes historical data, including customer and transactional information, to identify alerts that resulted in the generation of a Suspicious Activity Report (SAR). |
[Smart cluster labeling using generative AI](cluster-genai) | Use cluster insights provided by DataRobot with ChatGPT to provide business- or domain-specific labels to the clusters using OpenAI and DataRobot APIs. |
[Improve customer communication using generative AI](comm-genai) | How generative AI models, like GPT-3, can be used to augment predictions and provide customer-friendly subject matter expert responses. |
[Hyperparameter optimization workflow](hyperopt) | Build on the native DataRobot hyperparameter tuning by integrating the hyperopt module into DataRobot workflows. |
[Zero-shot text classification for error analysis](zero-shot) | Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models. |
[Optimize customer support workflows with generative AI](customer-support) | Use generative AI models to cater to level-one requests, allowing support teams to focus on more pressing and high-visibility requests. |
[Monitor generative AI with custom metrics](genai-metrics) | Monitor LLMs and generative AI solutions to measure alignment and ROI and to provide guardrails. |
