Databricks introduced Databricks Assistant Edit Mode, a transformative feature enabling users to apply AI-generated suggestions across multiple notebook cells simultaneously with a single prompt.
Editing notebooks traditionally involves moving between individual cells, making repeated manual changes, and verifying code consistency. Databricks Assistant Edit Mode simplifies this by understanding the entire notebook context and suggesting inline edits in response to a single user instruction.
During early testing, the feature reduced refactoring time by over 50 percent, delivering faster, more consistent editing that is easier to review.
How It Works
Users access Edit Mode via the Assistant side panel by selecting the Edit option and typing in a prompt. The Assistant then analyzes the notebook and presents inline suggestions. Recommendations can be reviewed and accepted or rejected either directly within the notebook or via the side panel. Additionally, users may apply all suggestions at once using Accept All or Reject All controls.
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Key Use Cases
Edit Mode is particularly effective in scenarios such as:
- Refactoring logic across cells by transforming repeated patterns into reusable functions and organizing intermediate steps more clearly
- Renaming variables and functions, with context-aware replacements applied only where needed
- Facilitating code migrations, including translating Pandas to PySpark, updating SQL dialects, and accommodating Delta Lake or Unity Catalog environments
- Standardizing code by fixing indentation, removing commented-out code, aligning quote styles, or substituting hardcoded values with parameters
- Generating unit test scaffolding by detecting core functions or transformations and suggesting tests with structure, inputs, and assertions
Future Enhancements
Databricks plans to enhance Edit Mode with the following capabilities:
- Introducing agentic workflows that allow the Assistant to comprehend broader user intent and drive high-level transformations rather than responding to isolated instructions
- Expanding Edit Mode support to AI/BI dashboards, enabling AI-powered suggestions across multiple SQL queries simultaneously
- Adding advanced tooling within the Assistant for requests such as adjusting cluster settings, scheduling jobs, and handling permissions
Edit Mode currently requires partner-powered models. Users are encouraged to visit the product page to explore the Databricks Assistant in action or consult the documentation for comprehensive details on available features.
