In the previous chapter, we assembled the foundational components for interacting with language models: prompts, models, and output parsers. These components allow for a single, structured call to an LLM. Most applications, however, require multiple steps. For instance, you might need to first generate a topic summary and then use that summary to write an article. This requires chaining operations together.

LangChain's "Chains" are designed for this purpose. They provide a standardized interface for linking components into a logical sequence. A single LLM call can be seen as a function $f$ that takes an input and produces an output.

$$\text{Output} = f(\text{Input})$$

Chains allow you to compose these functions, creating multi-step sequences where the output of one step becomes the input to the next, such as $g(f(\text{Input}))$.

This chapter covers how to construct these sequences. You will learn to:

• Use the basic LLMChain to connect a model, a prompt, and a parser.
• Build multi-step workflows using SequentialChain for executing operations in a fixed order.
• Introduce conditional logic into your workflows with RouterChain to select different paths based on the input.
• Apply these structures to build a content generation pipeline as a practical exercise.