Efficient data ingestion is foundational to modern AI-driven applications, yet developers face significant challenges: unstructured data, sensitive information management, and rising costs from excessive model fine-tuning. Fortunately, cloud-native Java runtimes like Quarkus simplify this process by seamlessly bridging data ingestion and AI workflows, primarily through Retrieval-Augmented Generation (RAG). In this hands-on technical workshop tailored for developers and AI engineers, we'll explore how Quarkus empowers teams to ingest, structure, and query data, making institutional knowledge instantly available to large language model (LLM) consumers.

Participants will:
* Structure Unstructured Data: Learn to extract actionable insights from PDFs, proprietary formats, and unstructured documents using the open-source Docling project, preparing your data for seamless AI integration.
* Deploy and Utilize RAG Effectively: Understand how RAG enables real-time retrieval and enhances generative responses without extensive fine-tuning. We’ll also cover targeted fine-tuning with InstructLab for specialized, domain-specific knowledge.
* Hands-On AI Development (Inner and Outer Loops): Practice building AI workflows locally using Podman AI Lab, ensuring sensitive data remains secure, and scale seamlessly into a Kubernetes-native environment, maintaining privacy and cost efficiency.

We'll culminate the workshop by constructing a practical, privacy-conscious application: a searchable, AI-powered ticketing solution inspired by systems like ServiceNow. Join us and discover how easily Quarkus and RAG can transform your raw data into secure, powerful, and instantly accessible business insights.