AI Debugging Techniques: Diagnosing AI System Issues

Debugging AI systems requires specialized techniques. Here’s how to diagnose and fix AI issues.

AI Debugging Framework

from dataclasses import dataclass
from typing import Dict, List, Optional
import json

@dataclass
class DebugSession:
    interaction_id: str
    input: str
    output: str
    context: Dict
    traces: List[Dict]

class AIDebugger:
    def __init__(self, ai_client):
        self.ai = ai_client
        self.trace_store = TraceStore()

    async def debug_interaction(self, interaction_id: str) -> Dict:
        """Debug a specific interaction."""
        # Retrieve interaction details
        session = await self.trace_store.get(interaction_id)

        analysis = {
            "interaction": session,
            "input_analysis": await self.analyze_input(session.input),
            "retrieval_analysis": await self.analyze_retrieval(session),
            "generation_analysis": await self.analyze_generation(session),
            "output_analysis": await self.analyze_output(session.output)
        }

        # Identify root cause
        analysis["diagnosis"] = self.diagnose(analysis)
        analysis["recommendations"] = self.generate_recommendations(analysis)

        return analysis

    async def analyze_input(self, input_text: str) -> Dict:
        """Analyze input for potential issues."""
        return {
            "length": len(input_text),
            "complexity": await self.estimate_complexity(input_text),
            "ambiguity_score": await self.check_ambiguity(input_text),
            "intent_clarity": await self.check_intent(input_text),
            "potential_issues": self.identify_input_issues(input_text)
        }

    async def analyze_retrieval(self, session: DebugSession) -> Dict:
        """Analyze retrieval step."""
        retrieval_trace = next(
            (t for t in session.traces if t["step"] == "retrieval"),
            None
        )

        if not retrieval_trace:
            return {"status": "no_retrieval"}

        return {
            "query": retrieval_trace.get("query"),
            "results_count": len(retrieval_trace.get("results", [])),
            "top_score": retrieval_trace.get("results", [{}])[0].get("score"),
            "relevance_scores": [r.get("score") for r in retrieval_trace.get("results", [])],
            "potential_issues": self.identify_retrieval_issues(retrieval_trace)
        }

    async def analyze_generation(self, session: DebugSession) -> Dict:
        """Analyze generation step."""
        gen_trace = next(
            (t for t in session.traces if t["step"] == "generation"),
            None
        )

        if not gen_trace:
            return {"status": "no_generation"}

        return {
            "model": gen_trace.get("model"),
            "prompt_tokens": gen_trace.get("prompt_tokens"),
            "completion_tokens": gen_trace.get("completion_tokens"),
            "temperature": gen_trace.get("temperature"),
            "finish_reason": gen_trace.get("finish_reason"),
            "potential_issues": self.identify_generation_issues(gen_trace)
        }

    def diagnose(self, analysis: Dict) -> Dict:
        """Diagnose root cause of issue."""
        issues = []

        # Check input issues
        if analysis["input_analysis"]["ambiguity_score"] > 0.7:
            issues.append({
                "type": "ambiguous_input",
                "severity": "medium",
                "description": "Input query is ambiguous"
            })

        # Check retrieval issues
        if analysis["retrieval_analysis"].get("top_score", 1) < 0.5:
            issues.append({
                "type": "poor_retrieval",
                "severity": "high",
                "description": "Retrieved documents have low relevance"
            })

        # Check generation issues
        if analysis["generation_analysis"].get("finish_reason") == "length":
            issues.append({
                "type": "truncated_output",
                "severity": "medium",
                "description": "Output was truncated due to token limit"
            })

        return {
            "issues": issues,
            "primary_cause": issues[0] if issues else None
        }

    def generate_recommendations(self, analysis: Dict) -> List[str]:
        """Generate fix recommendations."""
        recommendations = []
        diagnosis = analysis["diagnosis"]

        for issue in diagnosis["issues"]:
            if issue["type"] == "ambiguous_input":
                recommendations.append("Consider adding clarifying questions")
            elif issue["type"] == "poor_retrieval":
                recommendations.append("Review chunking strategy and embedding model")
            elif issue["type"] == "truncated_output":
                recommendations.append("Increase max_tokens or summarize context")

        return recommendations

Systematic debugging enables quick identification and resolution of AI issues.