Best AI Prompts for Code Refactoring with Google Antigravity
TL;DR
- Google Antigravity (the AI code completion and refactoring tool integrated into Google’s development environment) specializes in Python modernization and syntax cleanup.
- The most effective refactoring prompts specify the target code pattern, the desired refactoring goal, and the constraints around the change.
- Google Antigravity works best for systematic refactoring tasks that follow clear patterns, not one-off creative code redesigns.
- Automated refactoring prompts should always include a test requirement to prevent regressions.
- Python 2 to Python 3 migration, type hint modernization, and function decomposition are Antigravity’s strongest use cases.
Introduction
Every codebase accumulates technical debt over time. Functions that made sense during initial development become tangled as requirements evolved. Type hints that were omitted for speed become liabilities as the codebase scales. Python 2 code that was never migrated haunts projects that inherited it. The challenge is that refactoring is time-consuming, error-prone work that developers prefer to defer — until the debt becomes so burdensome that productivity grinds to a halt.
Google Antigravity addresses this by providing AI-assisted refactoring capabilities within the Google development environment. When used effectively, it can automate the systematic parts of refactoring — syntax modernization, type hint addition, function extraction — while alerting developers to the areas where automated refactoring is risky and human judgment is required.
Table of Contents
- What Google Antigravity Does for Python Refactoring
- Refactoring Prompt Foundations
- Type Hint Modernization Prompts
- Function Decomposition and Extraction
- Python 2 to Python 3 Migration
- Error Handling and Exception Refactoring
- Testing Integration Prompts
- Refactoring Safety and Review
- FAQ
- Conclusion
1. What Google Antigravity Does for Python Refactoring
Understanding Antigravity’s specific strengths and limitations shapes how you use it for refactoring work.
Strengths: Antigravity excels at systematic refactoring tasks where the input and output patterns are clear. Type hint addition, Python 3 syntax modernization, import reorganization, function docstring formatting, and simple function extraction are all well-suited to Antigravity assistance. It understands Python’s idioms and can suggest improvements that align with PEP 8 and PEP 484 conventions.
Limitations: Antigravity struggles with refactoring tasks that require understanding the broader system context — changing a function that is called in dozens of places, refactoring a pattern that requires business logic understanding, or modifying code where the automated change might affect behavior in subtle ways. For these tasks, human review remains essential.
Best Practice: Use Antigravity for the systematic, pattern-following refactoring tasks that are tedious for humans and well-suited to AI. Reserve human effort for the refactoring decisions that require architectural judgment.
2. Refactoring Prompt Foundations
The quality of Antigravity refactoring output depends almost entirely on how specifically you describe the target code and the desired outcome.
The Refactoring Prompt Template should include: the exact code or pattern you want to refactor, the specific refactoring goal (what should improve — readability, performance, type safety, idiomatic Python), any constraints (must preserve the existing function signature, must not change behavior, must be backward compatible), and the context for why this refactoring matters.
Basic Refactoring Prompt: “Refactor the following Python function to improve [readability/performance/type safety]. Preserve the function signature and behavior. Target Python [version — e.g., 3.9+]. Apply [specific refactoring technique — e.g., replace nested conditionals with early returns, extract repeated logic into helper functions, add type hints following PEP 484].”
[paste code here]Systematic Pattern Refactoring Prompt: “We have multiple instances of the following pattern throughout our codebase: [describe pattern — e.g., raw dict.get() calls without default values, strings concatenated with + instead of f-strings]. Generate a refactoring plan that: identifies all instances of this pattern in [specific file or module], applies the [specific improvement — e.g., adds .get() with default, converts to f-string], and verifies that the change preserves behavior.”
3. Type Hint Modernization Prompts
Adding type hints to Python code is one of Antigravity’s most practical refactoring applications. Well-typed Python is significantly easier to refactor safely.
Type Hint Addition Prompt: “Add comprehensive type hints to the following Python module following PEP 484 conventions and Python 3.9+ best practices. Use the modern ‘list[int]’ style rather than ‘List[int]’, include TypeVar and Generic where appropriate, and add docstring type annotations where runtime type checking is not feasible.”
[paste module code here]Partial Type Hint Refactoring Prompt: “This module has partial type hints. Identify which functions and classes lack type annotations and add them. For complex functions where type inference is ambiguous, add # type: ignore comments with an explanation of why the ambiguity exists. Preserve the existing type hints where they are correct.”
[paste module code here]TypedDict and Protocol Refactoring Prompt: “Refactor the following untyped data structures to use TypedDict for structured dictionaries and Protocol for interface definitions where appropriate. This will make the code more maintainable and enable static type checkers to catch errors.”
[paste code here]4. Function Decomposition and Extraction
Large, complex functions are a primary source of technical debt. Antigravity can help systematically decompose them into smaller, testable units.
Function Extraction Prompt: “Analyze the following function for extractable sub-functions. Identify logical units of work that could be separated into their own functions: name each extraction candidate, describe what it does and what it should accept/return, and refactor the original function to call these new helper functions while preserving the exact external behavior.”
[paste large function here]Early Return Refactoring Prompt: “Refactor the following function to use early returns for error/edge cases, reducing nesting depth. The goal is to make the happy path (the main logic) read left-aligned rather than increasingly indented. Preserve all behavior and the function signature.”
[paste function with deep nesting here]Method Chaining Refactoring Prompt: “Refactor the following sequence of operations on a data object to use a fluent method-chaining pattern where appropriate. Identify which operations can be chained and which should remain separate for readability.”
[paste sequential operations here]5. Python 2 to Python 3 Migration
Legacy Python 2 code is a significant maintenance liability. Antigravity can automate much of the mechanical conversion work.
Python 2 to 3 Syntax Migration Prompt: “Migrate the following Python 2 code to Python 3. Apply: print statement to print() function, old-style class definitions to new-style (inherit from object), relative import changes (add explicit relative imports with dots), string encoding fixes (decode/encode as needed), and xrange() to range(). Preserve the behavior exactly and add comments where the migration requires attention to detail.”
[paste Python 2 code here]Print Statement Migration Prompt: “Find all print statements in [file/module] and convert them to Python 3 print() function calls. For print statements with string formatting, convert to f-strings or .format() as appropriate for the Python version target.”
Division Operator Migration Prompt: “Review [file/module] for division operators that may have different behavior in Python 2 vs. Python 3 (integer division // vs. float division /). Add from future import division if mixed division is detected, and explicitly add // or / where the intent is unambiguous.”
6. Error Handling and Exception Refactoring
Exception handling patterns evolve as Python idioms mature. Refactoring error handling improves both readability and reliability.
Bare Except Clause Refactoring Prompt: “Find and refactor all bare except: clauses in [file/module] to catch specific exceptions. For each bare except: identify the most likely exception types it was meant to catch, replace with appropriate specific exceptions, and add logging or re-raising as appropriate for the context.”
[paste code with bare except clauses here]Exception Chaining Refactoring Prompt: “Review [file/module] for exception handling patterns that should use exception chaining (raise X from Y in Python 3). Add appropriate exception chaining to preserve the original traceback context while allowing custom exception types.”
Context Manager Refactoring Prompt: “Identify locations in [file/module] where resource acquisition and release are handled with try/finally instead of context managers. Refactor to use ‘with’ statements for cleaner resource management. For each change: verify the resource acquisition/release pattern is suitable for context manager conversion.”
[paste code with resource management here]7. Testing Integration Prompts
Refactoring without tests is risky. Antigravity can help generate or identify test coverage alongside refactoring.
Test Identification Prompt: “Before refactoring [function/class], identify the existing tests that cover this code. List: the test file and test function that exercises each code path, whether the existing tests would catch behavior changes from the planned refactoring, and what additional tests you would recommend writing before refactoring.”
Regression Test Generation Prompt: “Generate unit tests for the following function that cover: the primary use cases, the edge cases (empty inputs, boundary conditions), and the error paths. These tests should serve as regression tests for any subsequent refactoring.”
[paste function to be tested here]8. Refactoring Safety and Review
Automated refactoring requires systematic verification. Antigravity can help design safety checks.
Behavior Verification Prompt: “For each proposed refactoring in [file/module]: describe the behavior that must be preserved, identify which existing tests verify that behavior, and explain what could go wrong with the automated refactoring that tests would not catch.”
Refactoring Impact Assessment Prompt: “We are planning to refactor [describe refactoring scope]. Assess the risk level: what is the scope of the change, how many call sites are affected, what is the test coverage of the affected code, and what manual review steps are recommended before deploying the refactoring?”
FAQ
What types of Python refactoring is Google Antigravity best suited for? Type hint modernization, Python 2 to Python 3 migration, function extraction and decomposition, import reorganization, and docstring formatting are Antigravity’s strongest applications. These are systematic tasks with clear input/output patterns.
How do I prevent regressions when using AI-assisted refactoring? Always ensure existing tests pass before beginning a refactoring session. Request regression tests alongside refactored code. Run the full test suite after each significant refactoring change. Use a branch-based workflow so changes can be reviewed before merging.
Should I refactor code that is not broken? Refactor opportunistically when working in a area of code, not as a dedicated project. The best time to refactor is when you are already changing the code for a functional reason — fixing a bug, adding a feature — and the code’s context is fresh in your mind.
What is the difference between refactoring and rewriting? Refactoring changes structure without changing behavior. Rewriting changes behavior intentionally. If you are using AI to improve code and the primary goal is making it work differently, you are rewriting, not refactoring. Always use refactoring tools for structural improvements and rewrites require more careful human oversight.
Conclusion
Google Antigravity is a practical tool for automating the systematic, tedious parts of Python refactoring. Type hint modernization, Python 3 migration, and function decomposition are the areas where it delivers the highest value with the lowest risk. The key is matching the task to the tool’s strengths: systematic pattern-based refactoring for Antigravity, architectural judgment calls for human developers.
Your next step is to identify the module in your codebase with the highest concentration of technical debt and use the Type Hint Addition prompt to modernize its type annotations. Run your test suite afterward and measure the improvement in static analysis errors.