LLM Debugger
Systematically diagnose and fix LLM output issues.
Failure Taxonomy class FailureType(Enum): HALLUCINATION = "hallucination" FORMAT_VIOLATION = "format_violation" CONSTRAINT_BREAK = "constraint_break" REASONING_ERROR = "reasoning_error" TOOL_MISUSE = "tool_misuse" REFUSAL = "unexpected_refusal" INCOMPLETE = "incomplete_output"
Root Cause Analysis def diagnose_failure(input: str, output: str, expected: dict) -> dict: """Identify why LLM output failed"""
issues = []
# Check format
if expected.get("format") == "json":
try:
json.loads(output)
except:
issues.append({
"type": FailureType.FORMAT_VIOLATION,
"details": "Invalid JSON output"
})
# Check required fields
if expected.get("required_fields"):
for field in expected["required_fields"]:
if field not in output:
issues.append({
"type": FailureType.INCOMPLETE,
"details": f"Missing required field: {field}"
})
# Check constraints
if expected.get("max_length"):
if len(output) > expected["max_length"]:
issues.append({
"type": FailureType.CONSTRAINT_BREAK,
"details": f"Output too long: {len(output)} > {expected['max_length']}"
})
# Check for hallucination indicators
if contains_hallucination_markers(output):
issues.append({
"type": FailureType.HALLUCINATION,
"details": "Contains fabricated information"
})
return {
"has_issues": len(issues) > 0,
"issues": issues,
"primary_issue": issues[0] if issues else None
}
def contains_hallucination_markers(output: str) -> bool: """Detect common hallucination patterns""" markers = [ r'According to.that doesn\'t exist', r'In \d{4}.before that year', r'contradicts itself', ] return any(re.search(marker, output, re.IGNORECASE) for marker in markers)
Prompt Fixes PROMPT_FIXES = { FailureType.FORMAT_VIOLATION: """ Add strict format instructions: "Output MUST be valid JSON with this exact structure: ```json {{"field1": "value", "field2": 123}}
Do not add any text before or after the JSON." """,
FailureType.HALLUCINATION: """
Add grounding instructions: "Base your response ONLY on the provided context. If information is not in the context, say 'I don't have that information.' Never make up facts or details." """,
FailureType.CONSTRAINT_BREAK: """
Add explicit constraints: "Your response must be:
Maximum 200 words No code examples Professional tone only" """, FailureType.REASONING_ERROR: """ Add step-by-step reasoning: "Think through this step by step: First, identify... Then, consider... Finally, conclude..." """, }
def suggest_prompt_fix(diagnosis: dict, current_prompt: str) -> str: """Generate improved prompt based on diagnosis""" primary_issue = diagnosis["primary_issue"] if not primary_issue: return current_prompt
fix = PROMPT_FIXES[primary_issue["type"]]
return f"""{current_prompt}
IMPORTANT INSTRUCTIONS: {fix} """
Tool Improvements
```python def suggest_tool_fixes(diagnosis: dict, tool_schema: dict) -> dict: """Improve tool schema to prevent misuse"""
fixes = {}
for issue in diagnosis["issues"]:
if issue["type"] == FailureType.TOOL_MISUSE:
# Make required parameters more explicit
fixes["parameters"] = {
**tool_schema["parameters"],
"description": "REQUIRED: " + tool_schema["parameters"].get("description", "")
}
# Add examples
fixes["examples"] = [
{
"params": {"query": "example search"},
"description": "Use for finding information"
}
]
return {**tool_schema, **fixes}
Test Case Generation def generate_test_cases(diagnosis: dict, failed_input: str, failed_output: str) -> List[dict]: """Create regression test cases from failures"""
test_cases = []
# Test case for the specific failure
test_cases.append({
"input": failed_input,
"expected_behavior": "Should not " + diagnosis["primary_issue"]["details"],
"validation": lambda output: not has_same_issue(output, diagnosis),
})
# Edge cases based on failure type
if diagnosis["primary_issue"]["type"] == FailureType.FORMAT_VIOLATION:
test_cases.append({
"input": failed_input,
"validation": lambda output: is_valid_json(output),
"description": "Output must be valid JSON"
})
# Similar inputs that might trigger same issue
similar_inputs = generate_similar_inputs(failed_input)
for inp in similar_inputs:
test_cases.append({
"input": inp,
"validation": lambda output: not has_same_issue(output, diagnosis),
})
return test_cases
Debugging Workflow def debug_llm_output( prompt: str, output: str, expected: dict, context: dict = {} ) -> dict: """Complete debugging workflow"""
# 1. Diagnose issue
diagnosis = diagnose_failure(prompt, output, expected)
if not diagnosis["has_issues"]:
return {"status": "ok", "diagnosis": diagnosis}
# 2. Generate fixes
fixed_prompt = suggest_prompt_fix(diagnosis, prompt)
# 3. Create test cases
test_cases = generate_test_cases(diagnosis, prompt, output)
# 4. Test fix
test_output = llm(fixed_prompt)
fix_works = diagnose_failure(fixed_prompt, test_output, expected)["has_issues"] == False
return {
"status": "issues_found",
"diagnosis": diagnosis,
"fixes": {
"prompt": fixed_prompt,
"fix_verified": fix_works,
},
"test_cases": test_cases,
"recommendations": generate_recommendations(diagnosis, context)
}
Interactive Debugging def interactive_debug(): """Interactive debugging session""" print("LLM Debugger - Interactive Mode")
prompt = input("Enter prompt: ")
expected_output = input("Expected output format (json/text): ")
output = llm(prompt)
print(f"\nGenerated output:\n{output}\n")
if input("Does this look correct? (y/n): ").lower() == 'n':
diagnosis = diagnose_failure(prompt, output, {"format": expected_output})
print("\nDiagnosis:")
for issue in diagnosis["issues"]:
print(f"- {issue['type'].value}: {issue['details']}")
print("\nSuggested fix:")
print(suggest_prompt_fix(diagnosis, prompt))
Best Practices Reproduce consistently: Multiple runs Isolate variables: Test prompt changes one at a time Check examples: Are few-shot examples clear? Validate constraints: Are they explicit enough? Test edge cases: Boundary conditions Log everything: Inputs, outputs, issues Iterate systematically: Track what fixes work Output Checklist Failure taxonomy defined Root cause analyzer Prompt fix suggestions Tool schema improvements Test case generator Fix verification Debugging workflow Recommendations engine Interactive debugger Logging system