Review and Debug an AI Chatbot

Scenario

You are an engineer supporting an enterprise security and compliance platform. The platform includes a customer-facing AI Assistant that answers compliance questions (e.g., SOC 2 readiness, vendor risk assessments, policy gap analysis) using internal documents, customer evidence vaults, and external integrations (e.g., cloud provider APIs).

Recently, enterprise customers have reported several issues:

1. Factual Inaccuracies & Hallucinations: The bot asserts policies exist when they are missing or cites outdated evidence.
2. Cross-Tenant & Privacy Risks: Traces suggest potential leakage of sensitive tenant metadata or PII into logs or prompt contexts.
3. High Latency & Inconsistency: Response times often exceed 8 seconds, and identical queries yield contradictory guidance.
4. Tool Call Failures: Bot fails silently or enters retry loops when querying third-party security integrations.

Design a systematic framework to review, trace, debug, and safely remediate these failures across the pipeline.

Requirements

Functional & System Scope

• End-to-End Traceability: Map every step of the request lifecycle: query rewriting, tenant-scoped vector retrieval, prompt compilation, LLM tool-calling, post-processing/guardrails, and user rendering.
• Error Taxonomy: Build a structured error taxonomy categorizing failures (e.g., Retrieval Miss, Context Overflow, Tool Schema Violation, Guardrail Rejection, Hallucination).
• Privacy-Aware Observability: Design a logging and tracing architecture that enables deep debugging without storing plaintext customer secrets, PII, or raw compliance evidence in shared logs.
• Evaluation & Safe Rollout: Define automated offline evaluation benchmarks and online gating (canary/shadow deployments) before pushing model, prompt, or retrieval updates.

Scale & Constraints

• Workload: 10,000 active organizations; 2,000,000 queries/day (~50 queries/second average, 300 peak QPS).
• Latency Target: p95 under 2.5 seconds end-to-end.
• Zero-Tolerance Policy: Strict zero-tenant-crossover guarantee and compliance with SOC 2 / HIPAA auditability.

What Your Design Should Cover

• Pipeline Architecture: Flow of components and where span telemetry/checkpoints are captured.
• Telemetry Schema & Redaction: How spans correlate intermediate inputs/outputs while scrubbing customer PII/sensitive tokens.
• Root Cause Isolation: Concrete heuristics or classifiers to isolate whether a failure is due to retrieval recall, context truncation, LLM reasoning, or tool timeouts.
• Remediation Strategy: Controlled mitigations (e.g., deterministic fallbacks, prompt versioning, semantic caching, circuit breakers).