What is LangChain - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the PromptTemplate class from LangChain.

LangChain

from langchain.prompts import [1]

Drag options to blanks, or click blank then click option'

ALangChain

BChain

CPromptTemplate

DPrompt

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing it with 'Prompt' or 'Chain'.

Wrong module path.

PromptTemplate is a core class in LangChain for defining prompts.

2fill in blank

medium

Complete the code to create a simple PromptTemplate instance.

LangChain

prompt = [1].from_template('Tell me a joke about {topic}')

Drag options to blanks, or click blank then click option'

APromptTemplate

BLLMChain

CChatOpenAI

DChain

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting '.from_template'.

Using the wrong class.

Use PromptTemplate.from_template() to create prompts easily.

3fill in blank

hard

Complete the code to import LLMChain.

LangChain

from langchain.chains import [1]

Drag options to blanks, or click blank then click option'

ASimpleChain

BPromptChain

CLangChain

DLLMChain

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Chain' instead of 'LLMChain'.

Wrong import path.

LLMChain combines an LLM and a prompt to form a chain.

4fill in blank

hard

Fill both blanks to create an LLMChain (assume llm is defined).

LangChain

chain = [1](llm=llm, prompt=prompt)
response = chain.[2]('cats')

Drag options to blanks, or click blank then click option'

ALLMChain

Brun

CPromptTemplate

Dinvoke

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'invoke' (newer API) instead of 'run'.

Wrong parameters for LLMChain.

Create with LLMChain(llm=llm, prompt=prompt) and run with chain.run().

5fill in blank

hard

Fill all three blanks for a complete simple LangChain example (assume imports and llm done).

LangChain

prompt = PromptTemplate.from_template('[1] {topic}')
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.[2]('[3]'))

Drag options to blanks, or click blank then click option'

ATell me about

Brun

Ccats

Dexecute

Attempts:

3 left

💡 Hint

Common Mistakes

Wrong run method.

Mismatched input variable.

Incomplete template.

Use a template string with variable, run the chain, and provide input like 'cats'.

Practice

1. What is the main purpose of LangChain?

easy

A. To create databases for storing large text files

B. To design user interfaces for mobile apps

C. To help build applications that use language models easily

D. To compile programming languages into machine code

Solution

1. Step 1: Understand LangChain's role

   LangChain is designed to help developers build apps that use language models.

2. Step 2: Compare options

   Only To help build applications that use language models easily matches this purpose; others describe unrelated tasks.

3. Final Answer:

   To help build applications that use language models easily -> Option C

4. Quick Check:

   LangChain purpose = build language model apps [OK]

Hint: Remember LangChain connects language models to apps [OK]

Common Mistakes:

• Confusing LangChain with database tools
• Thinking LangChain is for UI design
• Assuming LangChain compiles code

2. Which of the following is the correct way to describe a 'chain' in LangChain?

easy

A. A database table storing user inputs

B. A single prompt sent directly to a language model

C. A programming language used to write LangChain

D. A sequence of steps connecting models, prompts, and tools

Solution

1. Step 1: Define 'chain' in LangChain context

   A chain is a workflow linking models, prompts, and tools in order.

2. Step 2: Eliminate incorrect options

   Options A, B, and D do not describe a chain correctly.

3. Final Answer:

   A sequence of steps connecting models, prompts, and tools -> Option D

4. Quick Check:

   Chain = workflow steps [OK]

Hint: Chains link multiple steps in LangChain workflows [OK]

Common Mistakes:

• Thinking a chain is just one prompt
• Confusing chains with databases
• Believing chain is a programming language

3. Given this LangChain code snippet, what will be the output?

from langchain import PromptTemplate, LLMChain, OpenAI
prompt = PromptTemplate(template="Translate '{text}' to French.", input_variables=["text"])
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run(text="Hello")
print(result)

medium

A. Hello

B. Error: Missing API key

C. Bonjour

D. Translate 'Hello' to French.

Solution

1. Step 1: Analyze the code's function

   The code sets up a prompt to translate text to French using OpenAI model.

2. Step 2: Consider runtime environment

   Without an API key set for OpenAI, the code will raise an error.

3. Final Answer:

   Error: Missing API key -> Option B

4. Quick Check:

   OpenAI needs API key to run [OK]

Hint: OpenAI calls require API keys or error occurs [OK]

Common Mistakes:

• Assuming output is translated text without API setup
• Thinking code prints original text
• Ignoring API key requirement

4. Identify the error in this LangChain code snippet:

from langchain import PromptTemplate, LLMChain
prompt = PromptTemplate(template="Say hello to {name}.", input_variables=["name"])
chain = LLMChain(prompt=prompt)
result = chain.run(name="Alice")
print(result)

medium

A. LLMChain missing llm argument

B. No error, code runs fine

C. Incorrect method name 'run' instead of 'execute'

D. Missing input_variables list in PromptTemplate

Solution

1. Step 1: Check PromptTemplate usage

   PromptTemplate requires input_variables list; it's missing here (but not fatal).

2. Step 2: Check LLMChain initialization

   LLMChain requires an llm (language model) argument, which is missing.

3. Final Answer:

   LLMChain missing llm argument -> Option A

4. Quick Check:

   LLMChain needs llm parameter [OK]

Hint: LLMChain always needs an llm argument [OK]

Common Mistakes:

• Ignoring missing llm argument
• Confusing method names
• Overlooking input_variables requirement

5. You want to build a chatbot using LangChain that answers questions and also fetches current weather data. Which approach best uses LangChain's features?

medium

A. Create a chain that connects a language model with a weather API tool

B. Use LangChain only for the weather API calls, ignoring language models

C. Write separate scripts for chatbot and weather, no chaining needed

D. Use LangChain to store weather data in a database

Solution

1. Step 1: Understand LangChain's chaining ability

   LangChain can connect language models with external tools in a chain.

2. Step 2: Match use case to chaining

   Combining chatbot (language model) with weather API in one chain fits LangChain's design.

3. Final Answer:

   Create a chain that connects a language model with a weather API tool -> Option A

4. Quick Check:

   LangChain chains link models and tools [OK]

Hint: Chains combine models and tools for smart apps [OK]

Common Mistakes:

• Using LangChain only for API calls without models
• Separating chatbot and weather logic unnecessarily
• Misusing LangChain as a database