> For the complete documentation index, see [llms.txt](https://docs.eduplex.eu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eduplex.eu/text-analysis/ai-tutor-rag-system/golden-case-clapnq.md). # Golden Case CLAPNQ **Goal**\ Benchmarking to compare our retrieval system. Evaluating the retrieval part of our RAG System with a publicly available dataset that has ideal structured texts.\ \ **Data**\ Subset of the CLAPNQ Dataset. 104 questions, 724 chunks.\ Only documents with less than 10 chunks per documents were selected and a random sample was taken from them.\ Documents have between 1 and 10 chunks. The chunk containing the answer to the question is always included.\ \ **Method/Approach**\ Retrieval evaluated for three different vector embedding models:\ • Sbert\ • OpenAi text-embedding-3-small\ • OpenAI text-embedding-3-large\ \ **Results**\ The best performance measured by nDCG was 79% at k=5,6, while by CG a very high performance of 96% was achieved at k=6. Results were as expected given that nDCG is a stricter measure of quality. No large differences were observed between sBERT and OpenAI embedding models for this dataset.\ \ **Evaluation Metrics**\ Ranking quality: nDCG (Discounted Cumulative Gain), measuring the best possible result considering the right ranking.\ Retrieval quality: CG (Cumulative Gain), measuring total relevance, without penalizing for lower ranking.\ \ **Conclusions**\ The experiment produced expected results. Cumulative Gain (CG) reached a high of 96% at k=6, showing strong overall retrieval performance across all models. Although OpenAI’s embeddings (both small and large) showed marginally better ranking quality than sBERT, the difference was not significant enough to justify switching from our current sBERT model. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://docs.eduplex.eu/text-analysis/ai-tutor-rag-system/golden-case-clapnq.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.