Improving Memory Retrieval: How New Computer achieved 50% higher recall with LangSmith

New Computer has developed Dot, a groundbreaking personal AI with a unique agentic memory system that evolves with user interactions by integrating verbal and behavioral cues into its long-term memory. By utilizing LangSmith, the team has significantly improved their memory retrieval system, achieving 50% higher recall and 40% higher precision compared to previous models. Dot's memory system dynamically creates documents for retrieval, incorporating meta-fields like status and dates for efficient query processing. To enhance this system, New Computer employed synthetic data and various retrieval methods, including semantic and keyword searches, demonstrating the effectiveness of LangSmith's SDK in rapid experimentation. They also refined Dot’s dynamic conversational prompts to improve response accuracy and adaptivity. As New Computer continues to innovate, their partnership with LangChain and use of LangSmith remain crucial in advancing Dot's ability to form deeper, customized interactions with users. Their recent launch has seen a high conversion rate to their app's paid tier, reflecting user expectations for adaptable AI that grows alongside them.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.