error handling

**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.