Building Chains: LLM, Sequential, and Router Chains
Explore techniques for constructing reusable and modular workflows in LangChain by combining LLMChain with SequentialChain and RouterChain. Understand how to handle multi-step processing, dynamic input branching, and summarize large documents using MapReduce and Refine chains. This lesson equips you to design scalable, maintainable LLM applications with effective chaining patterns.
In the previous lesson, you configured retrievers that pull relevant context chunks from a vector store. Those chunks, however, are only useful if they flow into a structured workflow that prompts an LLM, parses the output, and optionally feeds it into the next step. This is exactly what a chain does in LangChain. A chain is the framework’s core abstraction for composing a prompt template, an LLM call, and output parsing into a single reusable unit. Think of it like a function in programming: you define it once, pass in different inputs, and get consistent, structured outputs every time.
Chains matter because they move you beyond one-off prompts into structured, repeatable workflows. A customer support bot that classifies a ticket, drafts a response, and translates it into the user’s language is not one prompt. It is three steps wired together. Report generators, document summarizers, and multi-turn assistants all follow the same principle.
This lesson covers four chain patterns. You will start with LLMChain as the fundamental building block, compose multiple chains into a SequentialChain pipeline, add dynamic branching with RouterChain, and finish with MapReduceChain and RefineChain for summarizing documents that exceed an LLM’s context window.
LLMChain as the fundamental building block
An
PromptTemplate: Defines the text sent to the LLM, with placeholder variables (like ...