Iterations, hallucinations, and lessons learned: Rebuilding our AI Assistant on n8n

n8n embarked on an ambitious project to rebuild their internal AI assistant using their own low-code platform, emphasizing a shift from traditional coding to a workflow-based approach. The endeavor involved creating an AI assistant capable of debugging user errors, answering natural language questions, and assisting with credential setup, leveraging n8n's existing documentation and forum as knowledge bases. The team employed LangChain for orchestration and GPT-4 for processing, while experimenting extensively to fine-tune the AI's performance. A key challenge was ensuring that the AI provided accurate and contextually relevant responses, leading to the development of a "workflow info" tool to better interpret user queries. Through iterative testing and the introduction of a LangSmith-based validation system, n8n significantly enhanced the assistant's capabilities and response quality. The project not only demonstrated the potential of low-code solutions in AI development but also set the stage for further enhancements, such as integrating additional AI agents and exploring new large language models (LLMs). The success of this project has already started influencing the support team's operations and sparked interest in expanding the assistant's functionalities.

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