Distilabel-based Argilla 2.0 Chatbot: RAG over docs deployed to Hugging Face Space

AI Impact Summary

This article outlines building a domain-specific Argilla 2.0 chatbot by converting technical docs into a synthetic QA dataset with distilabel, followed by fine-tuning a domain-aware embedding model and a RAG-based retrieval setup. It uses llama-index and LangChain tooling to chunk and parse documentation, stores the final chatbot in a Hugging Face Space, and records interactions in Argilla for ongoing evaluation. The approach enables self-serve access to Argilla 2.0 documentation and continuous improvement via feedback telemetry, but it requires substantial compute (large LLM and embedding index) and governance over synthetic data quality and deployment reliability.