Case Study: AskRoger builds an AI content assistant with Activeloop Deep Lake

How AskRoger Built an AI Personal Assistant for Any Content

AskRoger, an AI-powered summary assistant, was grappling with the challenge of efficiently summarizing and answering questions over lengthy, multi-modal content like podcasts and white-papers. The context window limitations of standard large language models (LLMs) meant they couldn't process large documents, and responses were often inaccurate when they relied on the LLM's general knowledge instead of the specific source material.

The vendor, Activeloop, provided a solution using its Deep Lake vector database to enable Retrieval Augmented Generation. This allowed AskRoger to store content as embeddings, perform similarity searches for relevant content based on user queries, and feed that specific context to the LLM. The implementation resulted in more context-aware and relevant responses, significantly increasing user satisfaction. Activeloop's Deep Lake also provided a scalable, multi-modal database that was instrumental for further analysis and fine-tuning of the AI assistant.

AskRoger

Jean-Charles Touzalin

Founder