红帽全球峰会AI quickstarts
Explore a variety of innovative AI use cases from our community. These simple, focused examples have been designed for fast and easy deployment on the Red Hat AI platform.
This content is authored by Red Hat experts, but has not yet been tested on every supported configuration.
Centralize company knowledge with an Enterprise RAG Chatbot
Use retrieval-augmented generation (RAG) to enhance large language models with specialized data sources for more accurate and context-aware responses.
Build an AI-powered virtual agent
Build and deploy a conversational AI virtual agent on Red Hat OpenShift AI to automate customer interactions and provide instant support.
Accelerate Ansible troubleshooting with intelligent log analysis
Act on suggestions from an AI agent that identifies Ansible errors, routes them to the right experts, and offers guided remediation steps for faster resolution.
Deploy a privacy-focused AI assistant
Build a healthcare AI assistant that ensures your large language model has multiple layers of protection, including PII detection and content moderation.
Automate IT processes with Self-Service Agents - Laptop Refresh
Transform IT service delivery using AI to lower support effort, improve compliance, and increase throughput.
Serve a lightweight HR assistant
Replace hours spent searching policy documents with higher-value relational work.
Audit AI apps to meet compliance goals
Deploy a financial audit system with insights into the performance, cost, and usage of your large language models in real time.
Summarize and analyze your observability data
Create an interactive dashboard to analyze AI model performance and OpenShift cluster metrics using Prometheus.
Transform product discovery with AI recommendations
Integrate AI-driven product recommendations, automated review summarization, and enhanced search capabilities into an e-commerce storefront.
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