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AI Agents with LangChain & Autonomous Workflows · Lesson

Sequential & Simple Chains

Learn to construct basic chains for ordered execution of tasks, passing outputs from one step as inputs to the next.

Chains: Steps in Order

In LangChain, chains allow you to combine multiple Large Language Model (LLM) calls and other utilities into a single, coherent application.

Think of them as a series of steps where the output of one step becomes the input for the next. This creates a powerful, automated workflow.

Sequential Chains are a specific type designed for tasks that require a strict, ordered execution of steps.

Meet the LLMChain

Before we build complex sequential chains, let's look at the basic building block: the LLMChain.

An LLMChain combines an LLM (like GPT-4) with a PromptTemplate. It takes an input, formats it using the template, sends it to the LLM, and returns the LLM's response.

  • LLM: The language model that generates text.
  • PromptTemplate: Defines how user input is structured for the LLM.

All lessons in this course

  1. Introduction to LangChain Chains
  2. Sequential & Simple Chains
  3. Customizing Chain Logic
  4. Routing and Conditional Chains
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