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Parallel Chaining
Executes multiple independent prompts concurrently and intelligently combines their outputs, enabling faster processing and multi-perspective analysis that leverages parallel computation for complex tasks requiring diverse viewpoints or data sources
In 30 seconds
- What
- Runs multiple independent prompts at the same time, then combines their outputs into a single result.
- When to use
- Tasks needing diverse perspectives or data sources where waiting for one chain before starting another wastes time.
- Watch out
- Combining conflicting outputs from parallel chains often requires manual intervention or a clear aggregation rule.
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Parallel Chaining: Overview
Executes multiple independent prompts concurrently and intelligently combines their outputs, enabling faster processing and multi-perspective analysis that leverages parallel computation for complex tasks requiring diverse viewpoints or data sources
- Concurrent prompt execution
- Result aggregation strategies
- Load balancing and scaling
- Conflict resolution mechanisms
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References
The papers, specifications, and repositories this pattern is based on.
- Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022)arXiv:2203.11171
- A Systematic Survey of Prompt Engineering in Large Language Models (Sahoo et al., 2024)arXiv:2402.07927
- Chain-of-Thought Prompting Elicits Reasoning (Wei et al., 2022)arXiv:2201.11903
- Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)arXiv:2205.10625
- LangChain - How to Invoke Runnables in Parallel
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