Language models can produce impressive responses – but also hallucinations. In a project for Landinfo, the Norwegian Country of Origin Information Centre for the immigration authorities, we explored how state-of-the-art Retrieval-Augmented Generation (RAG) can be used to extract relevant, verifiable information quickly and accurately – without compromising on quality and trust.

By applying a sequential decision-making process for information retrieval – inspired by the Markov Decision Process (MDP) – we treat each search as a step in a continuous improvement loop. This provides a framework for optimizing each part of the retrieval pipeline, allowing better control over generation and dynamic adaptation of responses based on the available information.

In this session, you'll get insight into how Landinfo uses RAG and sequential decision-making to optimize information retrieval and knowledge access in demanding domains. We’ll demonstrate how the technology can be practically implemented to ensure precision and traceability.

This presentation is suitable for developers, technologists, and anyone curious about how generative AI can be applied in knowledge-intensive environments with high demands for traceability and precision. No prior knowledge is required.