CC BY 4.0Upravitelev, MaxMaxUpravitelevSchoppa, NaomiNaomiSchoppaKrauss, ChristopherChristopherKraussAn, Truong-SinhTruong-SinhAnDo, BachBachDoAziz, Md AbdulMd AbdulAziz2025-08-182025-08-182025-08-182025-08-14https://publica.fraunhofer.de/handle/publica/490661https://doi.org/10.24406/publica-506110.3390/engproc202510301810.24406/publica-5061We propose a method to address the challenge of course discovery on search platforms by employing large language models (LLMs) to parse extended search parameters from natural language queries. We developed a set of algorithms that augment a course search platform prototype by integrating an LLM-based assistant to facilitate 55,000 vocational training sessions. The developed method supports natural language queries and parses optional search parameters. For parameter optionality and to evaluate the feasibility of parameter parsing, we introduce a relevance check mechanism based on cosine similarity. The parsing process was conducted by using a guided generation strategy with grammarbased restrictions to limit the generation possibilities. The developed method enhanced the precision and pertinence of course searches.enguided generationstructured outputslarge language modelsjournal article