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.