--- name: llm-debugger description: Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors". ---

Systematically diagnose and fix LLM output issues.

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"

def diagnose_failure(input: str, output: str, expected: dict) -> dict:
    """Identify why LLM output failed"""

issues = []

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 = {
    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:

1. First, identify...
2. Then, consider...
3. Finally, conclude..."
4. """,
5. }

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} """

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", "") }

return {tool_schema, fixes} ````

def generate_test_cases(diagnosis: dict, failed_input: str, failed_output: str) -> List[dict]:
    """Create regression test cases from failures"""

test_cases = []

return test_cases ```

def debug_llm_output(
    prompt: str,
    output: str,
    expected: dict,
    context: dict = {}
) -> dict:
    """Complete debugging workflow"""

if not diagnosis["has_issues"]: return {"status": "ok", "diagnosis": diagnosis}

return { "status": "issues_found", "diagnosis": diagnosis, "fixes": { "prompt": fixed_prompt, "fix_verified": fix_works, }, "test_cases": test_cases, "recommendations": generate_recommendations(diagnosis, context) } ```

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)) ```

1. Reproduce consistently: Multiple runs
2. Isolate variables: Test prompt changes one at a time
3. Check examples: Are few-shot examples clear?
4. Validate constraints: Are they explicit enough?
5. Test edge cases: Boundary conditions
6. Log everything: Inputs, outputs, issues
7. Iterate systematically: Track what fixes work

• 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