Chapter 9
LangChain for Simple GenAI Pipelines
Learning Objective
Understand where LangChain fits and when it is enough.
What it means
LangChain is a framework for building LLM applications. It provides components for prompts, chains, retrievers, tools, and memory. It is useful when building flows such as prompt → retrieve → generate.
Why it matters
LangChain helps developers avoid writing everything from scratch. It provides standard building blocks for RAG, tool calls, prompt templates, and integrations.
Healthcare Example
A clinical FAQ assistant can use LangChain to retrieve policy sections and answer questions. If the workflow is mostly linear and does not require complex branching, LangChain may be sufficient.
Architecture Flow
Code: Prompt Template Style
template = """
You are a healthcare policy assistant.
Use only the provided context.
Context:
{context}
Question:
{question}
Answer:
"""
def build_prompt(context, question):
return template.format(context=context, question=question)
print(build_prompt("Policy says documentation is required.", "What is required?"))Common Mistakes
- Using LangChain for every problem without evaluating complexity.
- Building large agent systems as fragile chains.
- Not testing chain outputs.
- Not versioning prompts.
Interview Q&A
Q: What is LangChain used for?
A: LangChain is used to build LLM applications using reusable components such as prompt templates, retrievers, chains, tools, and memory.
Q: When is LangChain enough?
A: It is enough for simpler pipelines such as summarization, extraction, and basic RAG where the flow is mostly linear.
Architect Takeaway
LangChain is good for AI components and pipelines. It is not always the best orchestrator for complex multi-agent workflows.