LangChain agents in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding LangChain Agent Types

Which LangChain agent type is best suited for tasks that require calling multiple tools in a sequence based on user input?

A Agent that dynamically selects and chains multiple tools based on input.

B Conversational agent designed for chat-based interactions without tool usage.

C Agent with memory that stores past interactions but does not use tools.

D Zero-shot agent that uses a single tool without prior examples.

💡 Hint

Think about which agent can decide which tools to use and in what order.

LangChain agents that dynamically select and chain multiple tools are designed to handle complex tasks by calling different tools in sequence based on user input.

❓ Predict Output

intermediate

Output of LangChain Agent Tool Call

What will be the output of this LangChain agent code snippet when the input is 'Translate hello to French'?

Prompt Engineering / GenAI

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

def translate(text):
    translations = {'hello': 'bonjour'}
    return translations.get(text.lower(), 'unknown')

tools = [Tool(name='translator', func=translate, description='Translate English to French')]
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent='zero-shot-react-description', verbose=False)

output = agent.run('Translate hello to French')
print(output)

A bonjour

B Translate hello to French

C Error: Tool function not found

D unknown

💡 Hint

Check how the translate function maps 'hello' to French.

The translate function returns 'bonjour' for 'hello'. The agent calls this tool correctly, so the output is 'bonjour'.

❓ Hyperparameter

advanced

Choosing Temperature for LangChain Agent LLM

You want your LangChain agent to give very precise and consistent answers without creativity. Which temperature setting for the underlying LLM is best?

A Temperature = -1.0 to disable randomness.

B Temperature = 0.0 for deterministic output.

C Temperature = 0.7 for balanced creativity and accuracy.

D Temperature = 1.0 for maximum creativity.

💡 Hint

Lower temperature means less randomness.

Setting temperature to 0.0 makes the LLM output deterministic and precise, ideal for consistent agent responses.

❓ Metrics

advanced

Evaluating LangChain Agent Performance

Which metric is most appropriate to evaluate a LangChain agent's ability to correctly use tools and provide accurate answers?

A Perplexity of the underlying language model.

B BLEU score measuring text similarity to reference answers.

C Mean Squared Error between predicted and true values.

D Accuracy of tool usage and final answer correctness.

💡 Hint

Think about what matters for an agent using tools to answer questions.

Accuracy of tool usage and final answer correctness directly measures if the agent used the right tools and gave correct answers.

🔧 Debug

expert

Debugging LangChain Agent Tool Integration

Given this LangChain agent code, what is the cause of the error when running the agent?

Prompt Engineering / GenAI

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

def calculator(input):
    return eval(input)

tools = [Tool(name='calc', func=calculator, description='Performs calculations')]
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent='zero-shot-react-description', verbose=False)

result = agent.run('Calculate 2 + 2')
print(result)

A The tool name 'calc' is not recognized by the agent causing a lookup error.

B The agent cannot parse 'Calculate 2 + 2' because it expects 'calc 2 + 2'.

C The OpenAI LLM is not initialized with an API key, causing authentication failure.

D The 'calculator' function uses eval which is unsafe and blocked by LangChain.

💡 Hint

Check if the LLM initialization is complete and authorized.

Without a valid API key, the OpenAI LLM cannot run, causing the agent to fail.