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When an AI system answers a question, the quality of the answer does not only depend on the language model. It depends on which information the system retrieves – and how it evaluates that information.
Maximilian Schattauer presented at Conf42 Prompt Engineering 2025 on how retrievers and document ranking in AI systems can be systematically optimised. The talk built on our ongoing work at Perelyn: How do we ensure that the systems we develop for clients do not just deliver answers, but the right answers?
The question sounds simple. In practice, it is not. Documents carry different levels of relevance, contexts shift, and the order in which a model receives information influences the outcome. Without careful engineering, the result is systems that sound convincing but miss the point.
For us, prompt engineering is not an isolated topic. It is a component of system architecture – and therefore an engineering discipline, not a matter of style.
The recording of the talk is available on YouTube.
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Liliya Imasheva presented a validation pipeline for evaluating AI summaries at Conf42 Large Language Models 2026.
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At the IOCMA 2026 conference, Perelyn presented our method, which transforms graph data such as supply chains or networks so that AI models can learn more reliably from it, even over long distances.
