Chang She

More and more enterprises are post-training AI models to be custom tailored for their domain and their users. Success in model training depends critically on establishing the right data flywheel to improve iteration speed.

In domains like law, compliance, and tax, building enterprise-grade RAG means very large scale, spikey workloads, a focus on accuracy, and non-negotiable privacy. In this talk, we'll share war stories and battle scars of how Harvey has built the world's most advanced AI agents for the legal profession on top of a highly optimized retrieval architecture.

Training and fine-tuning models depends critically on how you construct your dataset. Part art, part science, we’ll share with you practical lessons in dataset construction at Character AI and how to build a data platform to support rapid iterative refinement of training data. For LLMs, data scale is much larger and workloads are more diverse. This is especially true for multimodal datasets.

From recommendation systems to LLM-based applications, vector search is a critical component of the modern AI workflow. Existing vector solutions are complicated to use, hard to maintain, and cost too much.

From recommendation systems to LLM-based applications, vector search is a critical component of the modern AI workflow. Existing vector solutions are complicated to use, hard to maintain, and cost too much. LanceDB is a free open-source vector store that can perform low latency vector search on...