How to Use Prompt Chaining for Better AI Responses

Prompt chaining means linking multiple prompts so the output of one becomes the input of the next. This helps break complex tasks into smaller steps, improving clarity and results from AI models.

Syntax

Prompt chaining involves creating a sequence where each prompt uses the previous output as input. The basic parts are:

• Initial prompt: The first question or instruction.
• Intermediate output: The response from the AI model.
• Next prompt: A new prompt that includes or builds on the previous output.

This process repeats until the final answer is reached.

python

def prompt_chain(prompts, model):
    response = None
    for prompt in prompts:
        if response:
            prompt = prompt.replace("{previous}", response)
        response = model(prompt)
    return response

Example

This example shows a simple prompt chain that first asks for a summary, then asks for keywords from that summary.

python

def fake_model(prompt):
    if "Summarize" in prompt:
        return "AI helps solve problems by learning patterns."
    if "Keywords" in prompt:
        text = prompt.split(':')[1].strip()
        return ', '.join(word for word in text.split() if len(word) > 3)

prompts = [
    "Summarize the text: AI is a field of computer science.",
    "Extract Keywords from the summary: {previous}"
]

final_output = None
for prompt in prompts:
    if final_output:
        prompt = prompt.replace("{previous}", final_output)
    final_output = fake_model(prompt)

print(final_output)

Output

helps, solve, problems, learning, patterns

Common Pitfalls

Common mistakes when using prompt chaining include:

• Not passing the previous output correctly, causing prompts to lose context.
• Making prompts too long or complex, confusing the model.
• Ignoring errors or unexpected outputs from intermediate steps.

Always check each step's output before continuing.

python

def wrong_chain(prompts, model):
    # This does not use previous output, so context is lost
    for prompt in prompts:
        response = model(prompt)
    return response

# Correct way includes passing previous output

def correct_chain(prompts, model):
    response = None
    for prompt in prompts:
        if response:
            prompt = prompt.replace("{previous}", response)
        response = model(prompt)
    return response

Quick Reference

• Start with a clear initial prompt.
• Use placeholders like {previous} to insert earlier outputs.
• Validate outputs at each step.
• Keep prompts simple and focused.
• Chain as many steps as needed to solve complex tasks.

Key Takeaways

Prompt chaining breaks complex tasks into smaller, manageable steps.

Always pass the previous output correctly to maintain context.

Keep each prompt clear and simple to avoid confusing the AI.

Check intermediate outputs to catch errors early.

Use placeholders like {previous} to link prompts smoothly.