Error handling in AI and software systems is the practice of detecting, managing, and recovering from failures and exceptions gracefully, ensuring the system remains stable and provides useful feedback rather than crashing or producing silently wrong results.

Error Categories in AI Systems

• API Errors: Rate limits (429), server errors (500/503), authentication failures (401/403), timeout errors. These require retry logic with backoff.
• Model Errors: Hallucinations, refusals, empty responses, format violations, or truncated outputs. These require validation and retry with modified prompts.
• Infrastructure Errors: Network failures, disk full, out-of-memory (OOM), GPU errors. These require resource monitoring and fallback strategies.
• Data Errors: Invalid input, missing fields, encoding issues, schema violations. These require input validation before processing.

Best Practices

• Catch Specific Exceptions: Handle each error type with appropriate recovery logic rather than catching all exceptions generically.
• Don't Swallow Errors: Always log or report errors — silently ignored exceptions are the hardest bugs to diagnose.
• Use Structured Error Responses: Return consistent error objects with error code, message, and suggested action.
• Fail Fast: Detect errors early (validate inputs upfront) rather than failing deep in the processing pipeline.
• Idempotent Recovery: Ensure retry and recovery operations are safe to repeat without side effects.

AI-Specific Error Handling

• Output Validation: Check model responses for expected format, length, and content before returning to the user.
• Guardrail Enforcement: Catch and handle safety filter activations, content policy violations, and refusals.
• Token Limit Handling: Detect context window overflow and implement strategies like truncation, summarization, or chunking.
• Streaming Error Recovery: For streaming LLM responses, handle mid-stream disconnections and partial responses.

Monitoring and Alerting

• Error Rate Tracking: Monitor error rates by type and trigger alerts when thresholds are exceeded.
• Error Budget: Define acceptable error rates (SLOs) and take action when the error budget is depleted.

Robust error handling is what separates demo-quality AI applications from production-grade ones — every edge case not handled is a potential user-facing failure.