Asset recommendations promise faster retrieval setup while sparse relevance labels limit what can be trusted.
ML engineers need defensible sources for Retrieval Augmented Generation, while the first ranking model must learn from incomplete evidence across mixed assets.
Who you’d be doing this for
“I spent two days picking sources, then the eval showed the assistant cited the wrong table.”
Heidi Roux · Senior ML Engineer
Builds Retrieval Augmented Generation applications over documents, tables, notebooks, and SQL queries for a regulated enterprise.
What is at stake
Eighteen ML engineers reported manual source selection and widely uneven answer grounding. You must weigh a useful first ranking model against evidence that is incomplete across mixed enterprise assets.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- fast setup vs defensible relevance
- mixed assets vs focused first version
- sparse labels vs measurable ranking quality
- pilot adoption vs controlled exposure
Why Databricks
Vector Embeddings, MLflow, and enterprise asset retrieval place this ranking capability directly inside governed Retrieval Augmented Generation workflows.
Written with these in mind
Not your kind of problem? 107 more at Databricks, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.