Module 4 Wrap-up: The Workflow Engineer

You have mastered the "Lang" in LangChain. You understand that a chain is a set of Pipes (|) that automate the hand-off between different AI nodes. From simple single-step runs to complex parallel aggregations, you now have the tools to build structured intelligence.

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Hands-on Exercise: The Triple-Threat Agent

1. The Goal

Build a chain that:

1. Takes a {topic}.
2. Generates a Fact and a Myth about that topic in parallel.
3. Pipes both into a final Synthesizer that writes a "Truth-Checking" blog post.

2. The Implementation Plan

1. Define fact_chain and myth_chain.
2. Wrap them in a dictionary (Parallel).
3. Pipe the result into a ChatPromptTemplate that accepts {"fact": ..., "myth": ...}.

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Module 4 Summary

• LCEL: The declarative pipe syntax using |.
• Sequential: Step A $\rightarrow$ Step B logic.
• Routing: If/Else logic to choose the best specialized prompt.
• Parallel: Running multiple checks or tasks at once to save time.
• Composition: The "Lego" style building block methodology.

Coming Up Next...

In Module 5, we leave the world of pure logic and enter the world of Data Ingestion. We will learn about Document Loaders and Text Splitters, and how to prepare massive amounts of raw data for AI analysis.

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Module 4 Checklist

• I can write a chain using the | operator from memory.
• I understand the difference between invoke() and batch() on a chain.
• I can describe a scenario where RunnableParallel is better than Sequential.
• I have implemented a fallback route in a RunnableBranch.
• I understand that a dictionary in an LCEL pipe is a RunnableParallel.