# LLM-based Evaluator for Context Relevance **Goal**\ Assessing how well an LLM can identify relevant context chunks given question-text pairs so it can be used in a second-step of our retrieval system to filtered out irrelevant candidates.\ \ **Data**\ Modules Agiles Mindset, Kritisches Denken. 20 test questions from each module.\ \ **Method/Approach**\ LLM-based relevance scores from comparing question-chunk pairs. Prompt taken from TruLens Context Relevance.The LLM rates the context text chunk from 1 to 10. The score is normalized to a 0-1 scale.\ LLM used: gpt-4-0125-preview\ \ **Results**\ Recall of 100% in both Agiles Mindset and Kritisches Denken data.\ Precision: AM (71%) KD(33%).\ Accuracy: AM (81%) KD (52%)\ \ **Evaluation Metrics**\ Accuracy: The percentage of correctly predicted values.\ Recall: True positives / sum of true positives and false negatives (actual positives). Crucial when cost of false negatives is high. (predicted as 0 when it is in fact relevant)\ Precision: True positive/ sum of true positives and false positives (Total predicted positives '1').Crucial when cost of false positives is high. (predicted as 1 when it is not relevant).\ \ **Conclusions**\ The LLM-based context relevance evaluator was very good at correctly predicting all relevant chunks as relevant (100% recall). However, accuracy and precision was very different depending on the data evaluated. It was less accurate in general for the case of Kritisches Denken (52% accuracy) than for Agiles Mindset (71% accuracy). --- # 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/llm-based-evaluator-for-context-relevance.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.