Claude Context

# Case Study This directory includes some case analysis. We compare the both method(grep + Claude Context semantic search) and the traditional grep only method. These cases are selected from the Princeton NLP's [SWE-bench_Verified](https://openai.com/index/introducing-swe-bench-verified/) dataset. The results and the logs are generated by the [run_evaluation.py](../run_evaluation.py) script. For more details, please refer to the [evaluation README.md](../README.md) file. - 📁 [django_14170](./django_14170/): Query optimization in YearLookup breaks filtering by "__iso_year" - 📁 [pydata_xarray_6938](./pydata_xarray_6938/): `.swap_dims()` can modify original object Each case study includes: - **Original Issue**: The GitHub issue description and requirements - **Problem Analysis**: Technical breakdown of the bug and expected solution - **Method Comparison**: Detailed comparison of both approaches - **Conversation Logs**: The interaction records showing how the LLM agent call the ols and generate the final answer. - **Results**: Performance metrics and outcome analysis ## Key Results Compared with traditional grep only, the both method(grep + Claude Context semantic search) is more efficient and accurate. ## Why Grep Fails 1. **Information Overload** - Generates hundreds of irrelevant matches 2. **No Semantic Understanding** - Only literal text matching 3. **Context Loss** - Can't understand code relationships 4. **Inefficient Navigation** - Produces many irrelevant results ## How Grep + Semantic Search Wins 1. **Intelligent Filtering** - Automatically ranks by relevance 2. **Conceptual Understanding** - Grasps code meaning and relationships 3. **Efficient Navigation** - Direct targeting of relevant sections