Prompt Orchestration

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

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Continue to Prompt Routing and Conditional Logic to build intelligent workflows.

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Why Routing Matters

A single prompt optimized for customer complaints will perform poorly on technical questions, and vice versa. Routing solves this by:

1. →Classifying the input first
2. →Selecting the specialized prompt for that classification
3. →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.

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Continue to Map-Reduce Prompting Patterns to handle large-scale AI processing.

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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

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Continue to RAG Fundamentals to build AI systems grounded in your own data.