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Best prompts for ChatGPT for coding using refactoring legacy code
12 practical, copy-ready prompts to guide AI assistants through common legacy-code refactoring tasks. Each prompt includes a concise explanation, a realistic example, and recommended AI models to use.
Claude Opus 4
Claude Sonnet 4
Gemini 2.5 Pro
GPT-5
Gemini 2.5 Flash
You're staring at a legacy codebase that looks like it was written by someone who clearly had different priorities than maintainability. You know AI could help you refactor this mess, but every time you ask ChatGPT for help, you get generic advice that doesn't quite fit your specific nightmare scenario. Sound familiar?
This collection of 12 battle-tested prompts transforms your AI assistant into a legacy code refactoring expert who actually understands the real challenges you face. From auditing entire repositories to safely migrating databases with zero downtime, each prompt gives you copy-ready instructions that produce actionable, production-ready solutions instead of theoretical fluff. Stop wrestling with vague AI responses and start getting the specific, practical guidance you need to turn that legacy monster into maintainable code.
1
Audit repo and produce a prioritized refactor plan
I will paste a short summary of a legacy repository and a list of constraints. Produce a prioritized refactor plan (high/medium/low), an estimated effort (hours) per task, the risk level, required tests to validate the change, and 3 example git commit messages per task. Keep tasks atomic and order them for minimal-risk incremental delivery. Use the constraints in your plan.
Provide a step-by-step refactor roadmap for a legacy repository given constraints (tests, deployment, risk). Include priority, effort estimate, required tests, and sample git commit messages.
2
Extract duplicated code into a single helper
I will paste multiple code snippets from the same project that look duplicated. Create a single helper function/class that replaces the duplication, show updated call sites, produce one unit test validating behavior, and provide a unified patch (git diff format). Explain why your extracted API is minimal and safe for incremental rollout.
Identify repeated code blocks and produce a refactor that extracts them into a reusable function/class, update call sites, and include a unit test and a patch diff.
3
Introduce unit tests for a legacy function
Given this legacy function, write a comprehensive set of unit tests (happy path and edge cases). Mock any external calls, show how to run tests locally (commands), and provide a small CI config snippet (GitHub Actions or similar) to run the tests on push.
Create unit tests (including edge cases) for a given legacy function, mock external dependencies, and provide a CI configuration snippet to run them.
4
Convert callback-style code to async/await safely
Convert these callback-style functions to async/await. Keep equivalent error-handling semantics, add a small suite of tests that prove parity, and provide a rollback plan (how to revert if issues found). Return transformed code and tests in ready-to-apply patch format.
Refactor Node.js callback-based functions to async/await, preserving error behavior and providing a migration test and step-by-step rollback plan.
5
Modernize language idioms (loops -> streams/ comprehensions)
Refactor the given code to use the modern language idiom (e.g., Java Streams, Python list comprehensions). Preserve behavior exactly (including order and mutability) and provide simple microbenchmark results or complexity analysis to ensure performance is not regressed.
Replace verbose legacy idioms with modern, idiomatic constructs (Java Streams, Python comprehensions, C# LINQ) while preserving behavior and performance characteristics; include before/after and microbenchmarks.
6
Split a monolithic file into cohesive modules
I will paste a large source file. Propose logical module splits with names and responsibilities, output the refactored module files with updated imports/exports, and provide a migration checklist with small, testable PR steps. Keep the public API stable between steps.
Analyze a large file and propose module boundaries, then produce refactored module files, updated imports, and a migration checklist to land changes incrementally.
7
Add gradual static typing (TypeScript / mypy migration)
Given this untyped source file and repository constraints, produce a typed version (TypeScript or Python type hints), explain how to configure the type checker to allow incremental adoption (tsconfig / mypy.ini), and include a CI job snippet to run the type checks only for changed files.
Recommend a minimal-viable typing strategy for a codebase, convert a sample file to typed form, and provide CI/type-check steps that allow incremental typing without breaking builds.
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