Implement a request batching system for LLM API calls to optimize throughput and costs. ## Requirements {{batching_requirements}} ## Current Traffic Pattern {{traffic_pattern}} ## Latency Constraints - Max batch wait time: {{max_wait_ms}}ms - Target batch size: {{target_batch_size}} Implement a complete solution: ```python class LLMBatcher: """ Implement: - Dynamic batch sizing - Timeout-based flushing - Priority queue support - Error handling per request - Metrics collection """ ``` Include: - Thread-safe implementation - Async/await support - Backpressure handling - Unit tests
LLM Request Batching System
Build a production-ready LLM request batching system with dynamic sizing, priority queues, and comprehensive error handling for cost and throughput optimization.
99 copies0 forks
Share this prompt:
Details
Category
CodingUse Cases
Request batchingThroughput optimizationCost reduction
Works Best With
claude-sonnet-4-20250514gpt-4o
Created Updated Shared