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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
🎯 30-Second Overview
Pattern: Execute multiple independent tasks concurrently to reduce latency
Why: Drastically reduces wall-clock time for I/O-bound operations, enables multi-perspective analysis
Key Insight: Fan-out independent tasks → aggregate results with voting/merging/synthesis strategies
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Multiple independent lookups (APIs, DBs)
- • Multi-perspective analysis tasks
- • I/O-bound operations with latency
- • Consensus-building scenarios
Avoid When
- • Sequential dependencies exist
- • Simple single-step tasks
- • Strict rate limits apply
- • Consistency > speed requirements
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022)
A Systematic Survey of Prompt Engineering in Large Language Models (Sahoo et al., 2024)
Chain-of-Thought Prompting Elicits Reasoning (Wei et al., 2022)
Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)
Contribute to this collection
Know a great resource? Submit a pull request to add it.
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
🎯 30-Second Overview
Pattern: Execute multiple independent tasks concurrently to reduce latency
Why: Drastically reduces wall-clock time for I/O-bound operations, enables multi-perspective analysis
Key Insight: Fan-out independent tasks → aggregate results with voting/merging/synthesis strategies
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Multiple independent lookups (APIs, DBs)
- • Multi-perspective analysis tasks
- • I/O-bound operations with latency
- • Consensus-building scenarios
Avoid When
- • Sequential dependencies exist
- • Simple single-step tasks
- • Strict rate limits apply
- • Consistency > speed requirements
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022)
A Systematic Survey of Prompt Engineering in Large Language Models (Sahoo et al., 2024)
Chain-of-Thought Prompting Elicits Reasoning (Wei et al., 2022)
Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)
Contribute to this collection
Know a great resource? Submit a pull request to add it.