Building Retrieval Agents on Databricks
Databricks
Last updated June 30, 2026
Building Retrieval Agents on Databricks is a free, two-hour, hands-on course on agents that answer from your own documents — retrieval-augmented generation, built as an agent. A large part of the work is preparing the source material: taking unstructured documents and turning them into a structured, retrievable form the agent can search. From there you assemble a retrieval agent that fetches relevant passages and grounds its responses in them. It assumes Python and a working grasp of generative AI and agents, and it runs on the Databricks platform.
What you'll learn
- What a retrieval agent is and how retrieval-augmented generation (RAG) grounds its answers
- Parsing unstructured documents into a structured, retrievable form
- Building a retrieval agent that fetches relevant context on demand
- Wiring retrieval into an agent's responses so answers stay grounded
- Doing it hands-on in Python on the Databricks platform
Frequently asked questions about Building Retrieval Agents on Databricks
Who is Building Retrieval Agents on Databricks for?
Developers comfortable with Python and the basics of generative AI and agents who want to build a retrieval (RAG) agent grounded in their own documents.
Is Building Retrieval Agents on Databricks free?
Yes — Building Retrieval Agents on Databricks is completely free to take.
What are the prerequisites for Building Retrieval Agents on Databricks?
Python; the basics of generative AI and agents; familiarity with Databricks. A free Databricks Academy account is required to start.
Do you need to code for Building Retrieval Agents on Databricks?
Yes — Building Retrieval Agents on Databricks involves hands-on coding.
Why we suggest this course
A focused, practical build for developers who want an agent that answers from their own documents, with useful attention to the often-skipped step of preparing unstructured data for retrieval. One thing to know: the exercises run on Databricks and need a free Academy account, so the workflow is specific to that platform.