Legacy code migration is tedious and expensive. Upgrading a framework version across thousands of files, converting callback-based code to async/await, or migrating from one ORM to another involves repetitive transformations that are too nuanced for simple find-and-replace but too tedious for manual conversion.

The AI Approach

LLMs can handle code transformation tasks that follow patterns. Feed the model examples of before-and-after code for your specific migration, then let it transform files in bulk. The model handles the nuances that regex-based tools miss: renaming variables to match new conventions, updating import paths, and adjusting function signatures.

Three-Step Build

  1. Define the migration - Write 5-10 examples of manual migrations you have already done. These before/after pairs become the few-shot examples in your prompt.
  2. Batch transform - For each file that needs migration, send it to the LLM along with your examples and instructions. Collect the transformed output.
  3. Validate - Run the test suite against each transformed file. Flag files where tests fail for human review. Files that pass tests can be committed directly.

Where It Breaks

Migrations that require understanding cross-file dependencies are harder. The model sees one file at a time and may miss that a renamed function is called from dozens of other files. Complex business logic embedded in legacy patterns may be transformed syntactically but lose semantic correctness.

The Production Path

Build a pipeline that processes files in dependency order, tracks which files have been migrated, and maintains a dashboard of migration progress. Pair AI transformation with comprehensive test coverage - the tests are your safety net. Plan for human review of the 10-20% of files where automated migration produces incorrect results.