# Retrieval of course candidates when searching via title. \ \ **Goal**\ To optimize the retrieval of relevant course candidates based on similar course titles.\ \ **Data**\ WBS data for 2136 modules, including title, product category, education goal, and learning goal.\ \ **Method/Approach**\ A random sample of 100 titles from the dataset was used as queries. Vector search was done on two different fields: (1) Title and (2) Combined title and learning goal. Three embedding models were tested: SBERT, OpenAI text-embedding-3-small, OpenAI text-embedding-3-large. Vector search replicated locally with FAISS using the same OpenSearch parameters (M=24, ef\_search=100, ef\_construction=128).\ \ **Results**\ The OpenAI 3-large model had the best retrieval scores for both the title-only search (0.92 at k=6) and when combining the title and learning goal (0.85 at k=6). The OpenAI 3-small model wasn’t far behind. SBERT struggled in both cases, showing the lowest scores (0.57 and 0.48 at k=6).\ \ **Evaluation Metrics**\ Retrieval quality: Cumulative Gain (CG at k=1 to k=6)\ \ **Conclusions**\ Results highlight the better retrieval effectiveness of OpenAI text embedding model 3-large, particularly at higher ranks, when searching on title and learning goal combined. In contrast, including the learning goal slightly decreased SBERT’s performance, possibly due to noise or irrelevant information that the embeddings struggled to handle effectively. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.eduplex.eu/text-analysis/ai-tutor-rag-system/retrieval-of-course-candidates-when-searching-via-title..md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.