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Parallelization

Parallelization divides subtasks of a larger problem and processes them simultaneously through separate LLM calls. The outputs are then aggregated to produce the final result.

This pattern has two primary variations:

  • Sectioning — Breaking a task into independent subtasks, each processed in parallel
  • Voting — Running the same task multiple times to generate diverse perspectives, then aggregating for higher confidence

Why It Matters

  • Increased speed — Parallel processing reduces latency by distributing workloads, ideal for time-sensitive tasks
  • Enhanced reliability — Multiple evaluations or diverse subtasks processed in parallel produce higher-confidence results
  • Focused task management — Specialized LLM calls handle each subtask, improving accuracy through focused attention
  • Scalability — Larger datasets or more complex workflows are handled without bottlenecking a single model

Key Components

Component Purpose Example
Parallel LLM Calls Each handles a different subtask (sectioning) or repeats the same task (voting) One call evaluates content for tone, another for factual accuracy, another for compliance
Aggregator Combines parallel outputs into a unified result (consolidating, voting on best, or integrating insights) Merges flagged issues from parallel code reviews into a comprehensive report
Input/Output Input initiates all parallel processes; output delivers aggregated results Input: a large dataset → Output: a combined analysis report

When to Use It

  • Speed-intensive tasks — Time-sensitive workflows requiring simultaneous processing
  • Tasks with multiple dimensions — Multiple independent considerations that can be evaluated separately
  • Higher confidence needs — Outputs requiring validation through multiple attempts or perspectives

Example: Market Research Analysis

A company needs comprehensive market analysis for a product launch covering competitor analysis, consumer trends, and regional insights:

Sectioning approach:

  1. Parallel Call 1 — Evaluates competitors' pricing and positioning
  2. Parallel Call 2 — Analyzes customer preferences and behaviors
  3. Parallel Call 3 — Studies market potential across regions
  4. Aggregator — Combines all findings into one comprehensive report

Voting approach (for uncertain predictions):

  1. Multiple models independently predict regional sales figures
  2. The aggregator evaluates and combines predictions for a well-rounded decision

Results:

  • Speed — All parts complete simultaneously instead of sequentially
  • Focus — Each call specializes in its area, improving depth and quality
  • Confidence — Voting reflects multiple viewpoints, reducing bias

How to Implement

  1. Identify subtasks — Break the problem into components that can be processed independently
  2. Determine parallelization type — Sectioning (dividing tasks) or voting (repeating with different approaches)
  3. Set up aggregation rules — Define how outputs combine (consensus, averaging, concatenating)
  4. Test and optimize — Ensure efficient operation and consistent, high-quality outputs

Based on Building Effective Agents by Anthropic.