LangChain Interview Questions

This document provides a curated list of LangChain interview questions commonly asked in technical interviews for LLM Engineer, AI Engineer, GenAI Developer, and Machine Learning roles.

This is updated frequently but right now this is the most exhaustive list of type of questions being asked.

Code Examples

1. Basic RAG Pipeline with LCEL

from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

vectorstore = FAISS.from_texts(["harrison worked at kensho"], embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()

retrieval_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

retrieval_chain.invoke("where did harrison work?")

2. Custom Agent with Tool Use

from langchain.agents import tool
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

@tool
def multiply(first_int: int, second_int: int) -> int:
    """Multiply two integers together."""
    return first_int * second_int

tools = [multiply]
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant"),
    ("user", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

agent_executor.invoke({"input": "what is 5 times 8?"})

3. Structured Output Extraction

from typing import List
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI

class Person(BaseModel):
    name: str = Field(description="The name of the person")
    age: int = Field(description="The age of the person")

class People(BaseModel):
    people: List[Person]

llm = ChatOpenAI()
structured_llm = llm.with_structured_output(People)

text = "Alice is 30 years old and Bob is 25."
structured_llm.invoke(text)

Questions asked in Google interview

• How would you design a production-ready RAG system?
• Explain query decomposition strategies for complex questions
• Write code to implement multi-vector retrieval
• How would you handle hallucination in production systems?
• Explain the tradeoffs between different chunking strategies
• How would you implement citation and source attribution?
• Write code to implement corrective RAG
• How would you optimize latency for real-time applications?
• Explain how to implement multi-modal document understanding
• How would you implement A/B testing for RAG systems?

Questions asked in Amazon interview

• Write code to implement a customer service chatbot with RAG
• How would you implement product recommendation using LangChain?
• Explain how to handle high-throughput scenarios
• Write code to implement semantic caching
• How would you implement cost optimization for LLM usage?
• Explain the difference between retrieval strategies
• Write code to implement SQL database agent
• How would you handle multiple document types?
• Explain how to implement batch processing
• How would you implement monitoring and alerting?

Questions asked in Meta interview

• Write code to implement content moderation with LangChain
• How would you implement multi-agent collaboration?
• Explain how to handle multi-turn conversations
• Write code to implement social content analysis
• How would you implement user intent classification?
• Explain the security considerations for LLM applications
• Write code to implement plan-and-execute agents
• How would you handle adversarial inputs?
• Explain how to implement guardrails
• How would you scale LangChain applications?

Questions asked in OpenAI interview

• Explain the LangChain ecosystem architecture
• Write code to implement advanced function calling
• How would you evaluate RAG system quality?
• Explain the differences between agent types
• Write code to implement autonomous task completion
• How would you implement self-healing agents?
• Explain how to optimize prompt engineering
• Write code to implement structured output extraction
• How would you handle context window limitations?
• Explain how to implement tool use evaluation

Questions asked in Microsoft interview

• Design an enterprise document Q&A system
• How would you integrate Azure OpenAI with LangChain?
• Explain how to handle rate limiting and quotas
• Write code to implement effective memory management
• How would you ensure data privacy in RAG applications?
• Explain the role of LangSmith in production monitoring
• Write code to implement a custom retriever
• How would you evaluate the faithfulness of generated answers?
• Explain strategies for reducing LLM costs
• How would you implement role-based access control?

Additional Resources

• Official LangChain Documentation
• LangChain Cookbook
• Pinecone Learning Center
• DeepLearning.AI LangChain Courses
• LangSmith Documentation