> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rork.com/llms.txt
> Use this file to discover all available pages before exploring further.
# How to get out of an error loop
> Sometimes, Rork will be unable to fix the error and/or start to hallucinate. In this doc, we will go over how to solve that
export const CtaBanner = () =>
Build your app with Rork today.
Ship iOS, Android, and web apps by chatting with AI.
;
## When the Model Hallucinates
Occasionally, the model may generate output that is incorrect, incomplete, or not grounded in your actual project state. This is referred to as **hallucination**.
Hallucinations usually appear as:
* Referencing files, APIs, or features that don’t exist
* Claiming something was “fixed” when nothing changed
* Repeating solutions that don’t resolve the issue
* Providing confident but incorrect instructions
This is expected behavior in AI systems and does not indicate a broken project.
## How to Resolve Hallucinations
### 1. Ground the Model With Facts
Provide **real, concrete inputs**:
* Error logs
* File contents
* Build output
* Exact error messages
Avoid vague prompts like:
> “It’s broken, fix it.”
Instead use:
> “Here is the full Expo prebuild error output. Fix only what’s causing this error.”
### 2. Constrain the Scope
Tell the model **exactly what it can and cannot change**.
Examples:
* “Only modify `app.json`.”
* “Do not add new libraries.”
* “Do not assume files that don’t exist.”
Clear constraints reduce hallucination significantly.
### 3. Force Verification
If the model claims something is fixed, require confirmation.
Use prompts like:
* “Show me exactly what changed.”
* “Which file was modified and why?”
* “Quote the line that fixes the error.”
If it cannot point to a real change, assume the fix is invalid.
### 4. Reset the Context
If hallucinations persist:
* Start a **new message**
* Paste only the **relevant files or logs**
* Restate the goal clearly
Long conversations increase the chance of drift.
### 5. Switch Models (If Available)
Different models reason differently. If one is looping or hallucinating:
* Switch models
* Re-paste the same grounded inputs
* Retry with tighter constraints
## When to Stop Iterating
Stop prompting the model if:
* It repeats the same fix multiple times
* It references non-existent files
* It contradicts itself across responses
At that point, move to:
* Manual inspection
* Logs-first debugging
* Native tools (Xcode, Expo logs)
## Best Practices to Avoid Hallucinations
* Always paste **real logs**
* Keep prompts short and specific
* Verify every claimed fix
* Treat AI output as a **suggestion**, not ground truth
## Key Principle
> The model is strongest when reacting to **real inputs**, not guessing missing context.
Hallucinations are a signal that the model needs **more constraints or better grounding**, not that your app is broken.