ai-resilience

To implement retry with exponential backoff for Gemini AI calls, you automate resilience patterns with exponential backoff and jitter for 429 and 5xx responses. This automatically retries transient network failures to ensure robust LLM integration.

Content-hash caching for AI integrations is a smart-skip mechanism that hashes inputs to avoid reprocessing identical requests. This optimizes token usage and reduces API latency by returning cached responses for duplicate queries.

To handle batch processing with bounded concurrency in TypeScript AI projects, you process workloads with strict concurrency limits to scale AI calls. This prevents overwhelming the API service while maintaining high throughput for batch jobs.

Yes, these resilience patterns work with both Python and TypeScript projects using google-genai. They provide production-ready AI call hardening, batch processing, and resilience auditing applicable across both language environments.

You need exponential backoff with jitter for LLM API rate limits to prevent thundering herd problems when services return 429 errors. Jitter randomizes retry intervals, distributing load and improving the success rate of subsequent requests.

You should not use smart-skip caching for AI calls when your application requires real-time data generation or dynamic context that changes per request. Content-hash caching assumes identical inputs yield identical outputs, which fails for time-sensitive queries.