A hallucinated module, a backfiring RAG pipeline and the MCP server to fix it (tmtabor.io)

At a recent workshop in San Diego, the team behind GenePattern unveiled GenePattern Copilot, their first generative AI initiative aimed at enhancing bioinformatics accessibility for non-technical scientists. During a demonstration, a crucial issue arose when the AI inaccurately listed a module called scPerturb, which does not exist. This incident highlighted the AI's tendency to hallucinate information, prompting a series of investigations and improvements to the underlying system. In addressing this hallucination problem, the team overhauled their retrieval-augmented generation (RAG) pipeline by implementing a new chunking strategy that resourced the knowledge base more efficiently. They transitioned to an LLM-based fact extraction method that ensures contextually accurate fragments are presented to the model. Additionally, the introduction of the GenePattern Model Context Protocol (MCP) server allows the assistant to retrieve an up-to-date list of available modules dynamically. This evolution transformed GenePattern Copilot from a basic Q&A tool into an advanced agent capable of executing tasks and interfacing with the platform, marking a significant advancement in AI-driven bioinformatics tools.