AI Prompts for Code Migration

Convert this Python 2 code to Python 3.

Industry surveys of migration projects keep finding the same thing: a large share — often close to half — run over schedule, and the delays rarely come from the obvious rewrites. They come from silent behavior changes, the subtle differences where migrated code does almost the same thing as the original, except in the edge cases nobody tested. AI prompts for code migration can move fast, but speed is worthless if the new code quietly behaves differently. Let's tear down a typical migration prompt and rebuild it to catch those silent changes.

Before: The Weak Prompt

Here's how most people start a migration:

Convert this Python 2 code to Python 3.

Or "convert this JavaScript to TypeScript," or "migrate this from library A to library B." The model will happily transform the code, and it'll usually look right. But "convert" tells the model to translate syntax, not to preserve behavior — and those aren't the same thing.

⚡ Pro tip: "Convert" and "preserve exact behavior" are different instructions. A conversion can look correct while changing how the code handles empty inputs, integer division, string encoding, or null values. Always prompt for behavior preservation, not just syntax translation.

Why It Fails

This prompt fails because migration is fundamentally about preserving behavior across a change of language or library, and "convert" only asks for surface translation.

The Python 2-to-3 example is famous for exactly this. Integer division changed (5/2 was 2 in Python 2, 2.5 in Python 3). String and bytes handling changed completely. A naive conversion translates the syntax but can silently alter what the code computes. The code runs, produces plausible output, and is subtly wrong.

Library migrations hide similar traps. Two libraries that do "the same thing" often differ in defaults — one sorts results, the other doesn't; one is case-sensitive, the other isn't; one returns null, the other throws. A conversion that swaps the library calls without accounting for these defaults produces code that works differently in ways that surface much later.

Then there's the testing gap. A weak migration prompt produces converted code with no way to verify it matches the original's behavior. You're trusting that "looks right" means "is right," which for migrations is a dangerous assumption.

⚠️ Common mistake: Trusting migrated code because it runs without errors. Running is a low bar for a migration. The question is whether it produces identical results to the original across all inputs, including edge cases. Code that runs but computes differently is the exact failure that blows up migration timelines.

After: The Improved Prompt

Here's the rebuild that targets behavior preservation:

Migrate this Python 2 function to Python 3. Requirements: - Preserve EXACT behavior, including edge cases. - Flag every place where Python 2 and 3 differ in a way that could change results: integer division, string/bytes handling, dict ordering, comparison behavior. Show how you preserved the original behavior at each. - Generate test cases that would catch a behavior change, including edge cases (empty input, zero, negative, unicode). - Note anything you're uncertain preserves exact behavior. Original code: [paste code]

What this does: It demands behavior preservation over syntax translation, forces the model to surface the specific known difference points between the versions, and generates tests designed to catch silent changes — turning a blind conversion into a verified migration.

Breaking Down Each Element

Each requirement targets a specific migration risk.

"Preserve EXACT behavior, including edge cases" reframes the whole task. The model now optimizes for equivalence, not just valid syntax, which changes how it handles the tricky spots.

"Flag every place where the versions differ" turns the model's knowledge of the migration's known gotchas into an explicit checklist. The Python 2-to-3 integer division trap, the string/bytes changes — these are documented differences the model knows about and will surface when asked, so you can verify each one.

"Generate tests that catch a behavior change" gives you the verification the weak prompt lacked. These tests, ideally run against both the original and migrated code, are how you prove equivalence instead of assuming it. Edge-case tests are where silent changes hide.

"Note anything you're uncertain about" surfaces the model's low-confidence spots, which are exactly where you should focus your review. A migration is a collaboration, and knowing where the model is unsure directs your attention efficiently.

⚡ Pro tip: When possible, run the generated tests against both the old and new code. If a test passes on the original but fails on the migration (or vice versa), you've found a behavior difference. This differential testing is the gold standard for verifying a migration preserves behavior.

Variations for Different Contexts

The skeleton adapts across migration types and roles.

A developer migrating frameworks (say, one web framework to another) should focus the prompt on differing defaults and lifecycle behavior: "note where the frameworks differ in middleware order, error handling, and request lifecycle." Framework migrations fail on the implicit behavior, not the obvious API.

A developer doing a database migration should prompt for data-integrity checks: "generate queries to verify row counts and key aggregates match before and after migration." Silent data loss is the nightmare scenario, and verification queries catch it.

A developer migrating a large codebase should prompt for an incremental strategy: "suggest how to migrate this in small, independently testable chunks rather than all at once." Big-bang migrations are where timelines die; incremental ones let you verify as you go.

Here's a reusable migration prompt skeleton:

Migrate [code] from [source] to [target]. - Preserve exact behavior; flag every known difference between [source] and [target] that could change results. - Generate tests that catch behavior changes, including edge cases. - Suggest an incremental migration order if the code is large. - Note anything you're unsure preserves behavior. [paste code]

What this does: The preserve-behavior-plus-flag-differences-plus-test structure targets silent changes directly, which is where migrations actually go wrong.

⚡ Pro tip: Migrate incrementally whenever you can. A migration done in small, tested chunks lets you catch behavior changes early and roll back cheaply. A big-bang migration that changes everything at once makes it nearly impossible to isolate which change caused a problem when something breaks.

Save and Reuse This

The weak prompt gave you a conversion that looked right and might behave differently. The strong one gives you a verified migration with the difference points flagged and tests to prove equivalence. The difference is entirely in prompting for behavior preservation instead of syntax translation.

That skeleton works across every migration you'll do, which makes it worth saving. PromptABCD is built for this — store your migration skeleton with the behavior-preservation and differential-testing requirements baked in, keep variants for language, framework, and database migrations, and reuse them on every project. Once "preserve exact behavior, flag the differences, generate catch-the-change tests" is a saved template rather than something you assemble from scratch under deadline pressure, your migrations stop running over schedule from silent surprises. And a migration that finishes on time because nothing silently broke is worth every second you spend prompting it carefully.

The psychological trap in migrations is that they feel low-risk because the code "already works." You're not writing new logic, just moving existing logic to a new home, so it seems safe. But that framing is exactly backwards. New code gets scrutinized and tested; migrated code gets waved through because it looks the same as what was there before. The silent behavior changes hide in that complacency. Treating a migration with the same rigor as new development — tests, review, verification of edge cases — is what separates the projects that finish on time from the ones that discover, three weeks in, that the new code has been computing subtly wrong answers the whole time.

⚡ Pro tip: Keep the original code available and runnable throughout the migration, not just as a memory. Being able to run both versions side by side on the same inputs is the fastest way to catch a behavior difference — you compare outputs directly instead of reasoning about whether they should match. Delete the old version only after the new one has proven equivalent on real data.

The teams that treat migration as a first-class engineering task — worthy of tests, review, and side-by-side verification — are the ones whose migrations become routine rather than dreaded. Each careful migration also builds a reusable playbook: the difference points you flagged, the tests you wrote, and the verification approach all transfer to the next migration of the same kind. What starts as a cautious, deliberate process becomes faster every time you do it, because the hard-won knowledge about where a given migration silently breaks is captured rather than rediscovered. That compounding is the real payoff of prompting migrations carefully instead of trusting a quick conversion. Start your next migration by writing down every known difference between the source and target, and let that list drive both your prompt and your tests. The migration that catches its silent changes early is the one that finishes when you said it would, and that predictability is worth far more than the illusion of speed a blind conversion offers. Prompt for equivalence, verify it, and ship with confidence.