prompt chaining

**Prompt chaining** is the **workflow pattern where outputs from one prompt stage become inputs to subsequent stages in a multi-step pipeline** - chaining decomposes complex tasks into manageable operations. **What Is Prompt chaining?** - **Definition**: Sequential orchestration of multiple prompt calls, each handling a specific subtask. - **Pipeline Structure**: Typical stages include extraction, transformation, reasoning, and final synthesis. - **Design Benefit**: Improves controllability compared with one large monolithic prompt. - **System Requirements**: Needs robust intermediate-state validation and error handling. **Why Prompt chaining Matters** - **Task Decomposition**: Breaks complex objectives into interpretable and testable units. - **Quality Control**: Intermediate checks catch errors before final output generation. - **Tool Integration**: Different stages can call specialized models or external tools. - **Maintainability**: Easier to optimize individual steps without full pipeline rewrite. - **Operational Flexibility**: Supports branching and fallback paths for unreliable stages. **How It Is Used in Practice** - **Stage Contracts**: Define strict input-output schemas for each prompt step. - **Validation Gates**: Apply format and semantic checks between chain stages. - **Observability**: Log stage-level metrics to diagnose latency and accuracy bottlenecks. Prompt chaining is **a fundamental orchestration approach for advanced LLM applications** - staged prompt pipelines improve reliability, debuggability, and extensibility for multi-step workflows.