Shipping GenAI isn't just models, it's 𝐫𝐞𝐩𝐞𝐚𝐭𝐚𝐛𝐥𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 with 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐩𝐚𝐭𝐡𝐬.
So 𝘧𝘳𝘰𝘮 𝘵𝘶𝘵𝘰𝘳𝘪𝘢𝘭 𝘵𝘰 𝘵𝘦𝘢𝘮 𝘱𝘭𝘢𝘺𝘣𝘰𝘰𝘬: I've distilled Databricks' unstructured data pipeline for RAG into a 𝒎𝒊𝒏𝒊𝒎𝒂𝒍, 𝒓𝒖𝒏𝒏𝒂𝒃𝒍𝒆 𝒘𝒂𝒍𝒌𝒕𝒉𝒓𝒐𝒖𝒈𝒉 your teams can drop into a UC-governed project and start measuring.
Highlights:
- widgets and a _bootstrap setup (that software developers might hate but data scientists will appreciate).
- gotchas: path configs and UC table naming.
- levers to tune: chunk size/overlap, model swaps.
Leader's angle: 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐬𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬, reduce setup drag, and enable fast iteration and comparison of results across projects and teams.
This is a walkthrough of the Databricks tutorials for setting up an unstructured data pipeline for RAG (retrieval augmented generation)…