# RAG Pipeline Optimization at Lemonade ## Company Background Lemonade is a technology-oriented insurance company serving millions of customers worldwide. The company is notable for its high degree of automation, with 50% of insurance claims being processed completely automatically. A distinguishing feature of Lemonade's approach is their chat-based UI, which has been in place since 2015 for all customer interactions including support and claims processing. ## RAG Pipeline Overview ### Pipeline Architecture The RAG pipeline at Lemonade consists of two main stages: - **Indexing Stage (Offline)** - **Query Stage (Online)** ## Common Challenges and Solutions ### Missing Content Issues - **Problem**: Relevant content not present in vector database - **Solutions**: ### Retrieval Challenges ### Top-K Retrieval Issues - **Problem**: Relevant documents not appearing in top-K results - **Solutions**: ### Reranking Problems - **Problem**: Relevant documents not prioritized correctly in final selection - **Solutions**: ### Response Generation Issues ### Information Extraction - **Problem**: LLM fails to extract correct information despite relevant context - **Solutions**: ### Output Format Control - **Problem**: Inconsistent formatting in responses - **Solutions**: ### Specificity Management - **Problem**: Mismatch between query specificity and response detail level - **Solutions**: ## Best Practices - **Systematic Troubleshooting** - **Data Quality** - **Pipeline Optimization** ## Technical Implementation Details ### Tools and Technologies - Vector databases for embedding storage - LLM APIs for processing and generation - Specialized tools for document processing - Custom evaluation and monitoring systems - Prompt engineering frameworks ### Evaluation Methods - Use of test cases and ground truth data - Automated evaluation pipelines - Context relevance scoring - Response quality assessment ## Production Considerations - **Scalability** - **Maintenance** - **Quality Control**