Below is a curated collection of PDF documents on advanced AI concepts, each accompanied by a brief summary and organized by publication date. ## **Mastering RAG, Galileo, September 2024** ![[Galileo_Mastering RAG.pdf]] "Galileo: Mastering RAG" explores the challenges and solutions associated with Retrieval-Augmented Generation (RAG) in the context of enterprise applications. As enterprises increasingly adopt large language models (LLMs), ensuring the accuracy and reliability of responses becomes paramount. The paper identifies common failure modes in RAG pipelines and introduces Galileo's approach to detecting, evaluating, and improving RAG systems, focusing on observability and rapid debugging. **Key Insights** - **RAG's Critical Role**: Retrieval-Augmented Generation enhances LLM outputs by grounding them in external knowledge. However, it brings new failure modes, including hallucinations, irrelevant context retrieval, and poor grounding in retrieved documents. - **Failure Modes in RAG**: - _Hallucinations_: Models fabricate responses unsupported by retrieved documents. - _Poor Retrieval_: Relevant documents are missed or irrelevant ones are retrieved. - _Prompting and Response Quality_: Misleading prompts or model misinterpretation of context can degrade answer quality. - **Galileo's Evaluation Framework**: - Galileo introduces a lightweight feedback loop to identify and mitigate RAG issues. - Evaluations focus on hallucination detection, context relevance, answer consistency, and precision/recall metrics for ground truth coverage. - Utilizes a “glass box” evaluation that allows granular analysis of components: queries, retrieved documents, and final answers. - **Automated Evaluation Tools**: - _Hallucination Detection_: Classifies if an answer is supported by retrieved documents. - _Context Relevance Scoring_: Assesses how well each chunk supports the answer. - _Precision and Recall_: Evaluates document retrieval accuracy using weak supervision when ground truth is available. - **Human-in-the-Loop Capabilities**: - Galileo offers a UI for subject-matter experts to provide feedback on retrieved documents and answers. - Feedback improves model retraining and prompt design. **Actionable Takeaways** - Implement automated evaluation systems to monitor hallucinations and retrieval quality in RAG pipelines. - Integrate human-in-the-loop processes to refine retrieval relevance and improve model training with expert feedback. - Use precision and recall metrics to assess and iterate over the retrieval component of RAG, especially in enterprise settings. - Design prompts and user queries with clarity to reduce misinterpretation and improve downstream generation quality. - Break down RAG system evaluation into subcomponents to isolate and address performance bottlenecks effectively. **Notable Quotes** - “Hallucinations are the top reason why enterprises do not trust Generative AI systems.” - “Precision and recall of retrieved documents... can be evaluated via weak supervision where ground truth is available.”