ChatGPT Truncating Code
You ask for a working file and get back a skeleton: two functions written out, then '// ... rest of your code here', then the closing brace. Or the response simply stops mid-function with no ending at all. These look like the same bug but they are two different problems. The placeholder version is a deliberate summarization habit, where the model decides that repeating unchanged code is wasteful and elides it. The hard stop is an output limit, where generation hit the maximum response length and was cut off mid-token-stream. The first is fixed by prompting and is entirely under your control. The second is fixed by asking for less at a time. Both get dramatically worse when you ask for a whole file in one shot, and both are dangerous when you paste the result back over working code, because a placeholder comment pasted into a real file deletes everything it stood in for.
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Symptoms You Might See
Responses contain '// rest of your code here' or '// ... unchanged' placeholders
The code block ends mid-function with no closing brace
Long files come back significantly shorter than what you provided
Asking for 'the full file' still returns an abbreviated version
Imports and helper functions silently disappear from the rewrite
The second half of a long response degrades into summary rather than code
Common Causes
The response hit the maximum output length
Every model has a cap on how much it can produce in one response, separate from how much it can read. When generation reaches that cap it stops wherever it happens to be, which in a long file is usually mid-function. Nothing is wrong with your prompt; you simply asked for more output than one response can hold.
The model is eliding unchanged code on purpose
Models are trained on code discussions where repeating an entire unchanged file is unhelpful, so they abbreviate with placeholder comments. From the model's perspective this is correct behavior. It becomes destructive only when you paste the output back verbatim.
The file is simply too large for a single-shot rewrite
Requesting a complete rewrite of a file of several hundred lines pushes against the output cap every time. Even when it fits, quality drops in the back half because the model is racing to finish.
The chat is long and context is crowded
Deep into a long conversation, earlier versions of the file, previous attempts, and error logs all compete for room. The model works from a partial view and reconstructs what it thinks the rest contained, dropping whatever it did not see.
The request was ambiguous about scope
'Fix the login bug' does not say whether you want a patch or a full file. The model picks, usually the shorter option, and abbreviates the parts it considers unrelated.
Step-by-Step Fixes
Forbid placeholders explicitly in the prompt
When to try: First, on every code request
Add to your request: 'Output the complete code with no placeholder comments. Never write "rest of the code here", "unchanged", or "...". If the output would be too long, stop at a clean boundary and I will ask you to continue.' Stating the exact forbidden strings works better than a general instruction to be complete.
Ask for a diff instead of a whole file
When to try: For any edit to an existing file
Request only the changed lines with a few lines of surrounding context, in unified diff format. This is usually a tenth of the output, so it never approaches the length limit, and it makes the change reviewable rather than requiring you to trust a full rewrite.
Say 'continue' when output stops mid-function
When to try: Immediately after a hard cutoff
If the response was cut off, reply with: 'Continue from exactly where you stopped. Do not restart or repeat anything already output.' The model resumes from the truncation point. Adding 'do not repeat' matters, because the default behavior is often to start over from the top and truncate again in the same place.
Request one function or one file at a time
When to try: When a whole-file request truncated twice
Break the work into pieces that each comfortably fit a single response: one component, one class, one module. Ask for them in sequence. Smaller units also mean better quality, because the model is not rationing its remaining output budget.
Never paste a response containing a placeholder
When to try: Every time, before pasting
Search the output for 'rest of', '...', and 'unchanged' before pasting anything over a real file. A single placeholder comment pasted into working code silently deletes everything it replaced, and the damage is often discovered hours later.
Start a fresh chat for large refactors
When to try: When quality degrades in a long session
In a long thread the model is working around old file versions and past attempts. Open a new chat, paste only the current file and the specific goal, and ask for the change. Truncation and elision both drop sharply with a clean context.
Use an IDE agent for whole-file edits
When to try: When you regularly edit files of several hundred lines
Tools that apply patches directly (Cursor, Claude Code, Copilot's edit mode) write into the file rather than printing it into a chat window, so the output cap does not apply the same way and unchanged code is never re-emitted. This removes the class of problem rather than working around it.
Ask for a completeness check before you use the code
When to try: Before pasting a large generated file
Follow up with: 'List every function in the file you just produced, and confirm none were omitted or abbreviated.' It is not a guarantee, but it catches obvious omissions cheaply, and the model will frequently notice and correct its own elision.
How to Prevent This
Put 'no placeholder comments, no ellipses' in your custom instructions so it applies to every chat
Default to diffs for edits and full files only for new code
Keep source files small enough that a rewrite fits comfortably in one response
Grep every generated block for 'rest of' and '...' before pasting it anywhere
When to Contact Support
There is rarely anything to report here, since output limits are documented behavior rather than a fault. Contact OpenAI support only if responses cut off after a few lines regardless of length, or if the same short request truncates in every new chat on multiple devices, which points at an account or delivery problem rather than the output cap. If you are hitting this through the API, check finish_reason on the response: a value of 'length' confirms you hit the token cap and should raise max output tokens or chunk the request.
Frequently Asked Questions
Why does ChatGPT write 'rest of your code here' instead of the actual code?▼
It is abbreviating deliberately, not failing. Models are trained on code conversations where repeating unchanged sections is noise, so eliding them looks like the helpful choice. It only becomes destructive when you paste the result over a real file. Forbid the specific strings in your prompt, or ask for a diff so there is nothing to elide.
How do I get ChatGPT to output the complete file?▼
Ask for a smaller unit and explicitly ban placeholders: 'Output the complete file with no abbreviations, no placeholder comments, and no ellipses.' If it is still too long, split the file and request it in named parts, or switch to an IDE agent that edits files in place instead of printing them into chat.
What do I do when the code just stops mid-function?▼
That is the output length limit rather than elision. Reply 'Continue from exactly where you stopped, do not repeat anything.' The model picks up at the truncation point. If it truncates again in the same place, the request is too big for one response and you need to split it.
Is truncation worse in long chats?▼
Yes. Old file versions, previous attempts, and error output crowd the context, so the model is reconstructing from a partial view and rationing its output. Starting a fresh chat with only the current file and the goal is the single most effective fix for a session that has degraded.
Does the API have the same problem?▼
The elision habit yes, the hard cutoff differently. In the API you control the maximum output tokens directly, and a truncated response reports finish_reason 'length', so you can detect it programmatically and continue by feeding back the partial output. In the chat interface you get no such signal, which is why you should check for placeholders manually.
Sources and verification
Product behavior and limits can change. These primary sources were used to verify this guide.
Other Common AI Errors
ChatGPT Not Working
Service Issues
🚦Rate Limit Exceeded
Usage Limits
📏Context Length Exceeded
Input Limits
🔥Model Overloaded
Service Issues