Empowering LLMs with your own data – RAG, function tooling, and fine-tuning in practice
Empowering LLMs with your own data – RAG, function tooling, and fine-tuning in practice
16/03/2026 - AI Infrastructure Summit 2026 - Munich
Abstract
Large Language Models like GPT, Claude, or LLaMA are powerful, but they don’t know your business, your products, or your data. Organizations now face a strategic choice: how to best combine proprietary knowledge with foundation models to deliver reliable, contextual, and secure AI applications. This session explores three key approaches, Retrieval-Augmented Generation (RAG), Function Tooling (Function Calling), and Fine-Tuning, and how each fits different business, technical, and compliance needs. Attendees will compare architectures, trade-offs, and real-world lessons learned when deciding between dynamic retrieval, external API orchestration, or model adaptation. Join this session and learn more about:
- How to architect LLM-powered systems that combine internal data and models for accuracy, compliance, and context-awareness
- When to use RAG, Function Tooling, or Fine-Tuning—and how to mix them effectively for dynamic, grounded, and secure AI behavior
- Best practices for evaluation, cost management, and maintenance as LLMs, tools, and business data continuously evolve
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