Prompt composition and chaining in LangChain

Introduction

Prompt composition and chaining help you build complex tasks by connecting simple prompts step-by-step. This makes your AI work smarter and more organized.

You want to break a big question into smaller, easier questions.

You need to use the answer from one prompt as input for the next.

You want to combine different AI tasks like summarizing and translating.

You want to create a workflow where each step depends on the previous one.

Syntax

LangChain

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

# Define individual prompt templates
prompt1 = PromptTemplate(input_variables=["name"], template="Hello {name}, how can I help you today?")
prompt2 = PromptTemplate(input_variables=["response"], template="You said: {response}. Can you tell me more?")

# Create chains for each prompt
chain1 = LLMChain(llm=llm, prompt=prompt1)
chain2 = LLMChain(llm=llm, prompt=prompt2)

# Chain them together
overall_chain = SimpleSequentialChain(chains=[chain1, chain2])

# Run the chain
result = overall_chain.run("Alice")

Each prompt template defines how to format the input for the AI.

Chains connect prompts so outputs flow from one to the next automatically.

Examples

This shows what happens if you give an empty input to a prompt chain.

LangChain

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

# Empty input example
prompt1 = PromptTemplate(input_variables=["text"], template="Summarize: {text}")
chain1 = LLMChain(llm=llm, prompt=prompt1)

# Running with empty string
result = chain1.run("")

Using just one prompt in the chain to keep it simple.

LangChain

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

# Single prompt chain
prompt1 = PromptTemplate(input_variables=["topic"], template="Explain {topic} simply.")
chain1 = LLMChain(llm=llm, prompt=prompt1)

result = chain1.run("photosynthesis")

This example chains two prompts so the second explains the first answer.

LangChain

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

# Chain with two steps
prompt1 = PromptTemplate(input_variables=["question"], template="Answer this: {question}")
prompt2 = PromptTemplate(input_variables=["answer"], template="Explain why: {answer}")

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

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

result = overall_chain.run("What is AI?")

Sample Program

This program first asks the AI to define a topic, then asks why that definition matters. It shows how chaining passes the first answer to the next prompt.

LangChain

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

# Initialize the language model
llm = OpenAI(temperature=0)

# Define the first prompt template
prompt1 = PromptTemplate(
    input_variables=["topic"],
    template="Give a short definition of {topic}."
)

# Define the second prompt template
prompt2 = PromptTemplate(
    input_variables=["definition"],
    template="Explain why this definition is important: {definition}"
)

# Create chains for each prompt
from langchain.chains import LLMChain
chain1 = LLMChain(llm=llm, prompt=prompt1, output_key="definition")
chain2 = LLMChain(llm=llm, prompt=prompt2, input_key="definition")

# Chain them together
overall_chain = SimpleSequentialChain(chains=[chain1, chain2])

# Run the chain with a topic
result = overall_chain.run("machine learning")

print("Final output:", result)

Important Notes

Prompt chaining helps organize complex tasks into simple steps.

Time complexity depends on the number of chained prompts and AI response time.

Common mistake: forgetting to pass output keys correctly between chains.

Summary

Prompt composition breaks big tasks into smaller prompts.

Chaining connects prompts so outputs flow smoothly.

This makes AI workflows easier to build and understand.

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