AI writes code for the wrong version: why it happens and how to pin versions

AI Writes Code for the Wrong Version

AI writing code for the wrong version is a bug where the model generates code based on how an older library version works, while your project runs a newer version. The code looks correct, follows proper syntax, uses function names that once existed, but crashes or worse—runs with incorrect results without reporting an error.

Three common forms:

• Function deleted or renamed: the old command no longer exists in the new version.
• Default values changed: the same command but with different behavior.
• Call method changed from synchronous to asynchronous: the code skips a waiting step so data comes back empty.

Because the code looks right, readers skim past it easily and miss the issue. The bug only shows up when running for real, sometimes after it's already deployed to the server.

A concrete example: a programmer asks ChatGPT or Claude to write code that fetches login cookies for a Next.js website. The code uses the synchronous call method from an old version, while the project has moved to Next.js 15 where this function became asynchronous. The page still builds on the developer's machine with test data, but when a real user logs in, they get back an empty value. This is why version bugs fall into the silent error category: no red warning appears to alert you.

Why does AI often write code for old versions?

The AI model learns from a limited dataset with a cutoff date, and documentation about old versions always outnumbers documentation about new versions. When a library releases a major new version this month, tutorials, forum questions, and sample code written for that version only existed for a few weeks, while the old version has years of accumulated material. When writing code, the model leans toward the approach that appears most often in its training data, and that approach usually belongs to the old version. Plus, the model doesn't know what today's date is or which library version is current unless the user or a tool provides that information. So lacking version information in your request means the model picks the most common version in its memory, not the version you're running.

An example from @0xCodio about Blender, open source 3D modeling software with a Python API: the identifier name for the EEVEE renderer changed from BLENDER_EEVEE to BLENDER_EEVEE_NEXT in version 4.2, then changed back to BLENDER_EEVEE in version 5.0. The model learned the name change in between correctly but got it wrong on the latest version, while a model that never learned about that intermediate change happened to be right by accident. The problem lies in aiming at the wrong target, not in reasoning ability.

How serious is this?

According to a benchmark that @0xCodio cited, a set of 30 Blender coding tasks was run on four Blender versions with ten models, totaling 1,190 answers scored by code execution. This data comes from the original author, and OPCAIOS has not independently verified it, but it shows a clear trend:

Metric	Blender 3.6	Blender 5.0
Rate of code that runs	91%	70%
Rate models correctly state what changed	81%	35%

Additionally, three task groups (compositor, boolean solver, sequencer) scored zero points with every model on Blender 5.0. One notable point: the rate at which models recognize they're using an old API drops sharper than the rate at which code runs, meaning AI doesn't warn itself when writing for the wrong version.

How to Pin the Version in Your Prompt?

Pinning the version means specifying the name and version number of every relevant tool right at the start of your request, then requiring the AI to state again which version it used for each important API. These two actions turn an implicit understanding into something verifiable.

A prompt header template that works for most projects:

Environment: Next.js 15.5, React 19, Node 22, TypeScript 5.
Only use APIs that exist in those versions.
After the code, list every API that changed from the previous major version
and make clear which version you are targeting.

With Claude Code or similar tools that can read your project directory, a better approach is to let the AI read package.json, requirements.txt, or your lock file itself. The real versions live in the file, so the AI does not have to guess and you do not have to type them again. For tools without a manifest file like Blender, running blender --version or python --version gives you the exact version number to paste into the start of your request. This approach takes less than a minute per work session, but eliminates the most common cause of version mismatch errors: the AI picks a default version from its training data instead of the actual version your project uses.

How do you get the machine to verify instead of the human eye?

Machine verification means every piece of code the AI writes must pass an automated test, and the test stops the process when something breaks. The human eye can easily miss version errors because the code looks reasonable, whereas the machine runs it and reports straight away.

The layers of checking should include, from cheap to expensive:

1. Type checking and compilation: catch functions that do not exist and wrong parameters before anything runs.
2. Test runs with exit codes: a broken script must make the system automatically report failure, not success.
3. Unit tests for critical sections: results match sample data.
4. Domain-specific validation: for example with 3D models for printing, check for open edges and degenerate faces with code, because the render still looks good even if the object is broken.

General principle: let the machine check what it can check, and use the human eye only for what the machine cannot measure. A detailed process for Blender is in the post AI writes Python for Blender.

Do you have a real example from upgrading to Next.js 15?

A close-to-home example for web makers: when opcaios.org upgraded to Next.js 15 and React 19, the team ran into exactly this kind of error. According to Next.js's official upgrade guide, from version 15 onward functions like cookies() and the params argument of pages become asynchronous and must use await. Sample code the AI generates from old habit still looks right, but when you test it you get errors or empty values.

The effective way to handle this is two steps: declare the Next.js version in your prompt, and run the TypeScript type checker after each AI edit. The type checker catches most missing await calls before the code goes to the server. This is our own experience upgrading, not general statistics.

When should you have the AI look up the docs instead of relying on memory?

Having the AI check official documentation makes sense when a library changes fast or just released a major version. Instead of relying on stale training data, the AI reads the current docs page and then writes code.

A common approach is to use MCP, a standard that lets AI applications read data and tools from outside sources. Some library providers run MCP servers that return documentation for the exact version you need. If you are new to this, read What is MCP? first. Note the security angle: an MCP server runs code or reads real data, so only install from trusted sources and grant read-only permissions first. Without MCP, the manual way still works well: open the library's upgrade guide, paste the breaking changes section into your request, and tell the AI to follow that list exactly. A list of changes written by the developers is more reliable than what the model remembers.

What checklist helps AI-written code match the version?

A checklist is a list of seven tasks in order that makes sure code the AI writes matches your actual version, and works for Next.js, Python, Blender, or any tool. Each step takes a few minutes but blocks a specific category of errors, from calling the wrong function to silently wrong results:

1. Write the exact version of every library in your prompt, or let the AI read your lock file.
2. Ask the AI to list which APIs changed and which version you are targeting.
3. Set up error handling so breakage actually stops the process.
4. Run the type checker or compiler after each AI edit.
5. Write at least one automated test for important results.
6. When something breaks, ask the AI to patch just the wrong part and return a diff, not rewrite the whole file.
7. For new or fast-changing libraries, have the AI check the current docs before writing code.

Frequently Asked Questions

Why does AI-written code have errors even though it looks correct?

The code looks correct because it matches common usage patterns in the data the AI learned from. Errors only appear when running on newer versions, where function names or calling conventions have changed.

Is specifying the version in the prompt enough?

Not quite. Specifying the version helps reduce errors but doesn't eliminate them completely. You need an additional step—running a test or type-checking—so the system can confirm the code actually works on that version.

Should I let AI read the documentation or trust the model's memory?

For stable libraries, the model's memory is usually sufficient. For libraries that just had a major release or are changing rapidly, you should let AI read current documentation, for example through MCP or by pasting relevant documentation pages into your request.

Do version errors only happen with one specific AI model?

No. According to benchmark tests on ten models that @0xCodio shared, the trend of lower scores on newer versions appears across many different models. Switching to a different model can't replace pinning the version and verifying it works.

Do non-technical people need to worry about this?

Yes, if you ask AI to write scripts to automate your work. Ask the AI which version it's targeting and request a test run before using it for real. If you want to learn how to use AI safely in your business, check out the AI application training course.

Source: benchmark data and Blender examples from @0xCodio's post on X (11/10/2026), not independently verified; Next.js 15 example is from OPCAIOS's experience.