Prompt composition and chaining in LangChain - Step-by-Step Execution

Concept Flow - Prompt composition and chaining

Define Base Prompts

↓

Compose Prompts Together

↓

Chain Prompts Sequentially

↓

Pass Output of One as Input to Next

↓

Get Final Combined Result

Start by creating simple prompts, then combine them into a chain where each prompt's output feeds the next, producing a final result.

Execution Sample

LangChain

from langchain import PromptTemplate, LLMChain

# Define two prompts
prompt1 = PromptTemplate(input_variables=["name"], template="Hello {name}!")
prompt2 = PromptTemplate(input_variables=["greeting"], template="{greeting} How can I help you?")

This code defines two simple prompts: one greets by name, the other adds a question using the greeting.

Execution Table

Step	Action	Input	Output	Next Input
1	Run prompt1 with name='Alice'	name='Alice'	Hello Alice!	greeting='Hello Alice!'
2	Run prompt2 with greeting='Hello Alice!'	greeting='Hello Alice!'	Hello Alice! How can I help you?	None
3	Chain complete	None	Hello Alice! How can I help you?	End

💡 All prompts executed in sequence; final output produced.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
name	None	Alice	Alice	Alice
greeting	None	Hello Alice!	Hello Alice!	Hello Alice!
final_output	None	None	Hello Alice! How can I help you?	Hello Alice! How can I help you?

Key Moments - 2 Insights

Why does the output of the first prompt become the input for the second?

Because in chaining, the output of one prompt is passed as input to the next, as shown in execution_table step 1 and 2.

Can prompts be chained in any order?

No, the order matters because each prompt may depend on the previous output, as seen in the flow from step 1 to step 2.

Visual Quiz - 3 Questions

Look at the execution table, what is the output after step 1?

A Hello Alice!

B Hello Alice! How can I help you?

C Alice

D How can I help you?

💡 Hint

Check the 'Output' column in row for step 1.

At which step does the final combined result appear?

A Step 1

B Step 2

C Step 3

D None

💡 Hint

Look at the 'Output' and 'Action' columns for step 3.

If the first prompt output changed, how would it affect the chain?

A No effect on the final output

B The second prompt input and final output change

C Only the first prompt output changes

D Chain breaks and stops

💡 Hint

Refer to how output of step 1 is input to step 2 in execution_table.

Concept Snapshot

Prompt composition and chaining:
- Define simple prompts with variables
- Combine prompts sequentially
- Output of one prompt feeds next
- Final output is combined result
- Order matters for correct chaining

Full Transcript

Prompt composition and chaining in Langchain means creating small prompts and linking them so the output of one becomes the input of the next. This lets you build complex conversations or tasks step-by-step. For example, a greeting prompt outputs 'Hello Alice!' which then feeds into a second prompt that adds 'How can I help you?'. The chain runs in order, producing a final combined message. This method helps organize prompts clearly and reuse parts easily.

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