We'll cover assembling gathered materials and structured data into an actual research dataset or database, building a small end-to-end pipeline you could rerun from scratch, and working iteratively from a simple version to a more complete one rather than trying to get everything right in a single pass. We'll also return to the habits of manual review and code review, and to the harder judgment calls: recognizing when an AI-assisted approach has gotten away from you.

Participants will come away with a reusable, end-to-end dataset-building workflow they can apply directly to their own research materials, plus a practiced sense of when to trust an AI assistant's output and when to step in and do the work by hand.