Prompt composition and chaining in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is prompt composition in Langchain?

Prompt composition is the process of building complex prompts by combining smaller prompt pieces. It helps create clear, reusable, and organized prompts for language models.

beginner

What does prompt chaining mean in Langchain?

Prompt chaining is linking multiple prompts so the output of one prompt becomes the input for the next. This creates a step-by-step flow of tasks for the language model.

intermediate

Why use prompt chaining instead of one big prompt?

Chaining breaks a big task into smaller steps. It makes prompts easier to manage, debug, and reuse. It also helps the model focus on one task at a time for better results.

intermediate

How do you pass data between prompts in a chain?

You pass data by using the output of one prompt as input variables for the next prompt. Langchain manages this flow automatically when you set up the chain.

intermediate

Name two common types of prompt chains in Langchain.

1. Sequential chains: prompts run one after another in order. 2. Conditional chains: next prompt depends on previous output conditions.

What is the main goal of prompt composition?

A To store prompts in a database

B To build complex prompts from smaller parts

C To train a new language model

D To translate prompts into other languages

Prompt composition means combining smaller prompt pieces to create complex prompts.

In prompt chaining, what happens to the output of one prompt?

A It is discarded

B It is saved as a file

C It becomes input for the next prompt

D It is sent to the user directly

The output of one prompt is used as input for the next prompt in a chain.

Which is a benefit of using prompt chaining?

A Breaks tasks into smaller steps

B Makes prompts harder to debug

C Requires more computing power

D Prevents reuse of prompts

Prompt chaining breaks big tasks into smaller, manageable steps.

How does Langchain handle data flow in prompt chains?

A Automatically passes outputs as inputs

B Saves data to a database

C Does not support data flow

D Manually by the user

Langchain automatically passes outputs from one prompt as inputs to the next.

Which is NOT a common type of prompt chain?

A None of the above

B Conditional chain

C Sequential chain

D Random chain

Random chain is not a common prompt chain type; sequential and conditional chains are common.

Explain how prompt composition helps when working with language models in Langchain.

Think about building with small blocks to make a bigger structure.

Describe the process and benefits of prompt chaining in Langchain.

Imagine passing a message along a chain of friends.

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