SledTrace – A local debugger that shows why your RAG app answered wrong (github.com)

SledTrace has launched as a local debugger specifically designed for Retrieval-Augmented Generation (RAG) applications. This tool allows developers to pinpoint why their apps return incorrect answers by displaying the chain of decisions made during the retrieval and response generation process. Users can easily set it up on their machines without needing an account or API key, streamlining the debugging process. The tool identifies discrepancies by comparing retrieved data with the model’s output, providing insights into issues such as conflicting information or outright inaccuracies in the generated answer. This release is significant for the AI/ML community as it enhances transparency and accountability in RAG models, which are increasingly used in various applications. SledTrace operates through deterministic heuristics that analyze text patterns locally, avoiding reliance on external evaluation of data. It tracks retrieval output, model prompts, and timing, all stored in an SQLite database for easy access. By enabling developers to understand where their models go wrong, SledTrace not only fosters better debugging practices but also aims to improve the overall reliability of AI applications. With support for Python and an emphasis on local operation, SledTrace promises to be a valuable tool for developers working with RAG frameworks.