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Sequential Chaining
A fundamental prompt engineering technique that breaks complex tasks into smaller, interconnected prompts where each output serves as input for the next step, creating a structured reasoning pipeline that significantly improves LLM performance on multi-step problems
In 30 seconds
- What
- Breaks a task into ordered prompts, each consuming the previous output as input for the next step.
- When to use
- Multi-step work where intermediate results need checking or refinement before proceeding to the next phase.
- Watch out
- Errors compound downstream; a mistake early on propagates through all subsequent steps unchecked.
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Video Explanation
Sequential Chaining: Overview
A fundamental prompt engineering technique that breaks complex tasks into smaller, interconnected prompts where each output serves as input for the next step, creating a structured reasoning pipeline that significantly improves LLM performance on multi-step problems
- Linear workflow execution with step-by-step processing
- Context preservation and state management across prompts
- Error isolation and recovery at each step
- Transparent reasoning process for debugging
- Reduced hallucination through focused prompts
- Improved accuracy on complex tasks vs monolithic prompts
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References
The papers, specifications, and repositories this pattern is based on.
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)arXiv:2201.11903
- Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)arXiv:2203.11171
- Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)arXiv:2205.10625
- A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024)arXiv:2402.07927
- LangChain Sequential Chains Documentation