Validate AI-Generated Code Before Trusting It
Test LLM outputs, run generated scripts with unit tests, inspect results, and reject failures — all in disposable, isolated environments. Build reliable AI coding assistants.
AI Code Looks Right, But Is It?
LLMs generate plausible-looking code, but they hallucinate, make subtle errors, and can produce insecure or broken implementations:
- Syntax errors that only appear at runtime
- Logic bugs that pass casual inspection
- Security vulnerabilities (SQL injection, path traversal)
- Hallucinated APIs that don't exist
The Solution: Validate Before Trust
- ✓Execute AI code in isolated sandboxes — catch runtime errors
- ✓Run unit tests automatically — verify correctness
- ✓Static analysis (linting, type checking) — catch bugs early
- ✓Feed errors back to LLM — automatic self-correction
The Validation Workflow
Generate
LLM generates code based on your prompt
Execute
Run code in isolated Hopx sandbox
Test
Execute unit tests and static analysis
Validate
Check outputs, errors, and test results
Iterate
Feed errors back to LLM for correction
Accept
Approve validated code for production
Why Validate AI Code with Hopx
Safe Execution
Run AI-generated code in isolated micro-VMs. No risk to your systems, even if the code is malicious or buggy.
Automated Testing
Execute generated code with unit tests and static analysis. Automatically reject code that fails validation.
Feedback Loop
Capture errors, tracebacks, and test results. Feed them back to the model for self-correction.
Trust Before Deploy
Validate that AI suggestions actually work before merging into production code.
Build Self-Correcting AI Pipelines
Create feedback loops where AI code is automatically tested, and failures are fed back to the model for correction. Ship reliable AI-powered features.
Works With Any LLM
OpenAI, Anthropic, open-source models — all compatible
Any Testing Framework
pytest, jest, go test — use your existing test setup
Iterative Refinement
Keep iterating until tests pass or max attempts reached
1from hopx_ai import Sandbox
2from openai import OpenAI
3
4client = OpenAI()
5sandbox = Sandbox.create(template="code-interpreter")
6
7def validate_ai_code(prompt: str, max_iterations: int = 3):
8 """Generate and validate AI code with feedback loop"""
9
10 for iteration in range(max_iterations):
11 # Generate code with LLM
12 response = client.chat.completions.create(
13 model="gpt-4",
14 messages=[{"role": "user", "content": prompt}]
15 )
16 generated_code = response.choices[0].message.content
17
18 # Execute in sandbox
19 result = sandbox.run_code(generated_code)
20
21 # Run tests
22 test_result = sandbox.run_code("""
23import pytest
24pytest.main(['/workspace/tests/', '-v'])
25""")
26
27 # Check if validation passed
28 if result.exit_code == 0 and 'FAILED' not in test_result.stdout:
29 return {
30 'success': True,
31 'code': generated_code,
32 'output': result.stdout,
33 'iterations': iteration + 1
34 }
35
36 # Feed errors back to model
37 prompt = f"""
38The previous code failed. Fix the errors:
39
40Code:
41{generated_code}
42
43Error:
44{result.stderr or test_result.stdout}
45
46Provide corrected code:
47"""
48
49 return {'success': False, 'error': 'Max iterations reached'}
50
51# Example usage
52result = validate_ai_code(
53 "Write a function to parse CSV and calculate averages"
54)
55
56if result['success']:
57 print(f"✅ Validated in {result['iterations']} iterations")
58 print(result['code'])
59else:
60 print("❌ Validation failed")
61
62sandbox.kill()