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Learn how to build enterprise-grade Retrieval Augmented Generation (RAG) systems for high-stakes domains like law, compliance, and tax through real-world experiences from Harvey's AI development team. Discover the unique challenges of scaling RAG for enterprise applications, including handling very large-scale, spikey workloads while maintaining accuracy and non-negotiable privacy requirements. Explore advanced retrieval techniques combining both sparse and dense retrieval methods to improve performance, understand why domain-specific reranking is essential for accuracy, and learn strategies for handling ambiguity in real-world user queries. Examine how LanceDB's search engine architecture enables low-latency, high-throughput retrieval across millions of documents of varying sizes while preserving privacy constraints. Gain insights into building AI systems that deliver highly accurate answers to hundreds of law firms and professional services firms across 45 countries, with practical war stories and lessons learned from developing the world's most advanced AI agents for the legal profession.
