Prompt Chaining & Pipelines: Building Multi-Step AI

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 honest read on prompt chaining vs. other orchestration patterns, tracked across r/LangChain, r/LocalLLaMA, and r/MachineLearning: chaining is the baseline pattern that outperforms mega-prompts on complex tasks, and the sharper community observation is that the gains come from constraint as much as from decomposition. Each step in a chain has a smaller context window to misinterpret, a narrower output format to respect, and a cheaper retry if it fails. The LangChain expression language docs, the LlamaIndex query pipeline, and the DSPy programming model all encode the same insight differently.

Where the community correctly pushes back on "chain everything" zealotry: chains multiply latency and cost linearly, and they fail more obscurely than single prompts — step 3 of 7 returns malformed JSON, the chain dies, and you have no idea which intermediate output was wrong unless you've been logging each step. The teams that run chains in production invest significantly more in observability (LangSmith, Langfuse, Helicone) than in the chain logic itself.

Pragmatic rule from engineers who ship prompt chains: keep chains short (2-4 steps for most tasks), log every input and output, and design every step to fail loudly and recoverably. The moment a chain gets past 5 steps you're building a workflow engine, and at that point you should evaluate whether LangGraph, Temporal, or a plain state machine is a better fit than stacking more prompts.

The Four Chain Patterns

Building Your First Chain

Error Handling in Chains

Advanced: Parallel and Loop Patterns

Test Your Understanding

Further Exploration

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.