Why Chain Prompts?
A single prompt that tries to do everything fails in predictable ways: it forgets constraints, mixes up sections, and produces inconsistent quality. Chaining solves this by giving each step a focused job.
Think of it like an assembly line. One worker who builds an entire car from scratch makes mistakes. A team of specialists — each doing one thing excellently — produces a perfect car every time.
The Four Chain Patterns
Building Your First Chain
Error Handling in Chains
Advanced: Parallel and Loop Patterns
Test Your Understanding
Next Steps
You now know how to build multi-step AI pipelines. In the next article, you will learn prompt routing — using conditional logic to dynamically choose which prompt runs based on input characteristics.
- →Agent Architecture Patterns — Prompt Chaining as the first pattern in agent architectures
Continue to Prompt Routing and Conditional Logic to build intelligent workflows.
Why Routing Matters
A single prompt optimized for customer complaints will perform poorly on technical questions, and vice versa. Routing solves this by:
- →Classifying the input first
- →Selecting the specialized prompt for that classification
- →Processing with the optimal prompt/model combination
The Three Routing Patterns
Pattern 1: Classification-Based Routing
Pattern 2: Confidence-Based Routing
Building a Complete Router
Advanced: Fallback and Error Paths
Test Your Understanding
Next Steps
You now know how to build intelligent routing systems. In the next article, you will learn the Map-Reduce pattern — processing large datasets by breaking them into chunks, processing in parallel, and merging results.
Continue to Map-Reduce Prompting Patterns to handle large-scale AI processing.
The Map-Reduce Pattern
Use Case: Document Summarization
Error Handling in Map-Reduce
Advanced: Cascading Map-Reduce
Test Your Understanding
Next Steps
You now command the full prompt orchestration toolkit: chaining, routing, and Map-Reduce. In the next module, you will learn RAG (Retrieval-Augmented Generation) — the technique that gives AI access to YOUR data by combining retrieval with generation.
- →Agent Architecture Patterns — The Map-Reduce pattern in the context of agent architectures
Continue to RAG Fundamentals to build AI systems grounded in your own data.