The Parallelization pattern distributes tasks across multiple LLM calls that can run simultaneously. This approach is particularly effective for processing multiple independent items or when different aspects of a task can be handled concurrently, significantly reducing overall processing time.
How it works
Task Decomposition: Break the problem into independent subtasks
Concurrent Execution: Dispatch all subtasks to LLM instances simultaneously
Result Aggregation: Collect and merge outputs from all calls
Synthesis: Combine results into final unified output
Key trade-offs
Latency: Reduced for batch workloads, increased for single items
Cost: Higher per-request but potentially lower total time
Consistency: May vary across parallel calls
Complexity: Requires result aggregation logic
Use cases
Batch processing of multiple documents or data points simultaneously
Multi-aspect analysis where different perspectives can be evaluated in parallel
Concurrent generation of multiple variations or alternatives
Distributed content processing for large-scale data analysis
Build This Pattern
Copy this prompt and paste it into Claude Code, OpenCode, Codex, or Cursor to implement this pattern.
Build me a parallel LLM processing system that fans out a task to multiple LLM calls simultaneously and aggregates their results. Architecture: implement a fan-out pattern where a dispatcher module sends the input task to N parallel workers (configurable, default 3). Each worker processes the same input with different temperature settings or focus areas. Workers run as independent async tasks. An aggregator module collects all responses and uses an LLM call to merge or summarize them into a single coherent output. Use Promise.allSettled for parallel execution with proper typing. Error handling: handle partial failures gracefully - if a worker times out (configurable per worker, default 15s) or returns an error, complete aggregation with remaining successful workers. Implement a minimum worker threshold: if fewer than half the workers succeed, return an error instead of a degraded result. Edge cases: handle all workers failing by returning a clear failure message. Support idempotent worker execution for retry scenarios. Handle aggregator LLM failure by falling back to a simple concatenation strategy. Best practices: use per-worker timeouts to prevent cascading delays. Return metadata showing which workers succeeded, failed, or timed out. Make worker count, temperature range, and focus areas configurable. Testing: unit test each worker in isolation. Test partial failure scenarios by mocking worker timeouts. Test aggregation with varying numbers of successful workers (0 to N). TypeScript.