一种基础的提示工程技术,它将复杂任务拆分为更小、相互衔接的提示,每一步的输出作为下一步的输入,从而构建出结构化的推理流水线,显著提升 LLM 在多步骤问题上的表现
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模式： 将复杂任务拆分为聚焦步骤的线性流水线
原因： 降低模型认知负担、隔离错误，并支持工具集成与分步缓存
关键洞察： 以结构化数据（JSON）实现 Output[N] → Input[N+1]，每一步均可缓存
Discover how Sequential Chaining relates to other patterns
Prerequisites, next steps, and learning progression
No prerequisites needed - great for beginners!
Execute multiple chains concurrently for speed and multi-perspective analysis
💡 Natural next step to add concurrency to your linear chains
Parallel processing followed by aggregation - perfect evolution of sequential chains
💡 Scale your sequential logic to handle parallel data streams
Distribute sequential steps across multiple agents and gather results
💡 Transform linear chains into parallel distribution patterns
Fork sequential tasks into parallel subtasks and join when complete
💡 Add sophisticated parallel execution with synchronization points
DAG-based workflows with complex branching logic
💡 Advanced evolution for complex non-linear workflows
Single-prompt step-by-step reasoning without explicit chaining
💡 Simpler approach when you don't need separate API calls for each step
Interleaves reasoning and action steps with tool use
💡 Better when you need dynamic tool usage throughout the workflow
Multi-stage analysis and decision-making workflows using sequential chains
Sequential processing for document analysis and knowledge extraction
Code analysis and generation workflows
通过这些精选资源加深理解
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)
Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications (2024)