Prompt composition and chaining in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a prompt template with a variable called 'name'.

LangChain

from langchain.prompts import PromptTemplate

prompt = PromptTemplate(input_variables=["name"], template="Hello, [1]!")

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

Alocation

Bage

Cname

Dgreeting

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name in the template that is not listed in input_variables.

Forgetting to use curly braces around the variable in the template.

The variable in the template must match the input variable name, which is 'name'.

2fill in blank

medium

Complete the code to chain two prompts where the output of the first is input to the second.

LangChain

from langchain.chains import SimpleSequentialChain

chain = SimpleSequentialChain(chains=[chain1, [1]])

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

Achain2

Bchain3

Cchain4

Dchain5

Attempts:

3 left

💡 Hint

Common Mistakes

Using a chain variable that is not defined.

Mixing the order of chains.

To chain two prompts, you list the chains in order. The second chain is 'chain2'.

3fill in blank

hard

Fix the error in the prompt template by completing the missing input variable.

LangChain

prompt = PromptTemplate(input_variables=[[1]], template="What is the capital of {country}?")

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

A"city"

B"state"

C"capital"

D"country"

Attempts:

3 left

💡 Hint

Common Mistakes

Listing a variable in input_variables that is not used in the template.

Forgetting to include the variable used in the template.

The template uses {country}, so 'country' must be in input_variables.

4fill in blank

hard

Fill both blanks to create a chain that takes input 'question' and outputs 'answer'.

LangChain

from langchain.chains import LLMChain

chain = LLMChain(llm=llm, prompt=prompt, output_key=[1], input_key=[2])

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

A"answer"

B"question"

C"response"

D"query"

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping input_key and output_key values.

Using keys that don't match the prompt variables.

The output_key should be 'answer' and input_key should be 'question' to match the chain's data flow.

5fill in blank

hard

Fill all three blanks to create a prompt template with variables 'topic' and 'style', and chain them correctly.

LangChain

from langchain.prompts import PromptTemplate
from langchain.chains import SimpleSequentialChain

prompt1 = PromptTemplate(input_variables=[[1]], template="Write about [2].")
prompt2 = PromptTemplate(input_variables=["style"], template="Make it [3].")

chain1 = LLMChain(llm=llm, prompt=prompt1)
chain2 = LLMChain(llm=llm, prompt=prompt2)

chain = SimpleSequentialChain(chains=[chain1, chain2])

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

A"topic"

Btopic

C"style"

Dstyle

Attempts:

3 left

💡 Hint

Common Mistakes

Not quoting variable names in input_variables list.

Mismatching variable names between template and input_variables.

input_variables must be a list of strings, so 'topic' is in quotes. The template uses {{topic}} and {{style}} variables.

Practice

1. What is the main purpose of prompt composition in Langchain?

easy

A. To run multiple AI models simultaneously

B. To break a big task into smaller, manageable prompts

C. To store data in a database

D. To create user interfaces for AI

Solution

1. Step 1: Understand prompt composition

   Prompt composition means dividing a large task into smaller prompts to handle each part separately.

2. Step 2: Identify the main purpose

   This helps make complex AI tasks easier to manage and understand by working on smaller pieces.

3. Final Answer:

   To break a big task into smaller, manageable prompts -> Option B

4. Quick Check:

   Prompt composition = breaking big tasks [OK]

Hint: Think of splitting a big job into small steps [OK]

Common Mistakes:

• Confusing prompt composition with running multiple models
• Thinking it stores data instead of organizing prompts
• Assuming it builds user interfaces

2. Which of the following is the correct way to chain two prompts in Langchain?

easy

A. chain = Chain([prompt1, prompt2])

B. chain = Chain(prompt1, prompt2)

C. chain = Chain.compose(prompt1, prompt2)

D. chain = Chain().add(prompt1).add(prompt2)

Solution

1. Step 1: Recall chaining syntax

   In Langchain, chaining prompts is done by creating a Chain object and adding prompts step-by-step.

2. Step 2: Identify correct method

   The method .add() is used to add prompts to the chain, so chaining looks like Chain().add(prompt1).add(prompt2).

3. Final Answer:

   chain = Chain().add(prompt1).add(prompt2) -> Option D

4. Quick Check:

   Use .add() to chain prompts [OK]

Hint: Look for method chaining with .add() calls [OK]

Common Mistakes:

• Passing prompts as a list directly to Chain
• Using Chain(prompt1, prompt2) without .add()
• Assuming a compose method exists

3. Given the code below, what will be the output of final_output?

prompt1 = Prompt(template="Hello, {name}!")
prompt2 = Prompt(template="How can I help you today?")
chain = Chain().add(prompt1).add(prompt2)
final_output = chain.run({"name": "Alice"})

medium

A. "Hello, Alice! How can I help you today?"

B. "Hello, {name}! How can I help you today?"

C. "Hello, Alice!"

D. Error: Missing input for prompt2

Solution

1. Step 1: Understand prompt templates and chaining

   Prompt1 uses the variable {name} which is replaced by "Alice". Prompt2 is a fixed string without variables.

2. Step 2: Analyze chain.run behavior

   Running the chain passes the input to prompt1, producing "Hello, Alice!" then continues to prompt2, appending "How can I help you today?".

3. Final Answer:

   "Hello, Alice! How can I help you today?" -> Option A

4. Quick Check:

   Chained prompts combine outputs [OK]

Hint: Chained prompts concatenate outputs with variables replaced [OK]

Common Mistakes:

• Thinking prompt2 needs input variables
• Expecting placeholders to remain unreplaced
• Assuming only first prompt output is returned

4. What is the error in the following code snippet?

prompt1 = Prompt(template="What is your name?")
chain = Chain()
chain.add(prompt1)
chain.run()

medium

A. Missing input arguments for run()

B. Chain object cannot be empty

C. Prompt template syntax is incorrect

D. add() method does not exist on Chain

Solution

1. Step 1: Check run() method usage

   The run() method requires input arguments matching prompt variables. Here, run() is called without arguments.

2. Step 2: Confirm prompt template variables

   Prompt1 has no variables, so no input is needed. But if prompt1 expected variables, missing inputs cause error.

3. Final Answer:

   Missing input arguments for run() -> Option A

4. Quick Check:

   run() needs inputs if prompts have variables [OK]

Hint: Check if run() has required inputs for prompts [OK]

Common Mistakes:

• Assuming run() works without inputs always
• Thinking add() method is missing
• Believing prompt syntax is wrong without variables

5. You want to create a chain where the output of the first prompt is used as input for the second prompt. Which approach correctly achieves this in Langchain?

hard

A. Create two independent chains and merge their results after running

B. Run prompt1 and prompt2 separately and concatenate their outputs manually

C. Use a chain that passes output variables from prompt1 to prompt2 as input

D. Use prompt2 with fixed text ignoring prompt1 output

Solution

1. Step 1: Understand chaining with variable passing

   To pass output from one prompt to another, the chain must connect outputs as inputs for the next prompt.

2. Step 2: Identify correct chaining method

   Langchain supports chaining where prompt2 receives variables produced by prompt1 automatically within the chain.

3. Final Answer:

   Use a chain that passes output variables from prompt1 to prompt2 as input -> Option C

4. Quick Check:

   Chaining passes outputs as inputs between prompts [OK]

Hint: Chain outputs flow as inputs to next prompt [OK]

Common Mistakes:

• Running prompts separately without chaining
• Merging results manually instead of chaining
• Ignoring output-input flow in chains