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Prompt Chaining

3 min read

This tutorial looks at Prompt Chaining in AI / LLMs.

This Prompt Chaining tutorial answers the question ‘What is prompt chaining?’.

Table of Contents

  1. What is Prompt Chaining?
    (Definition, purpose, and key characteristics.)
  2. What problem does Prompt Chaining solve?
    (Pain points and why it’s needed.)
    )
  3. How does Prompt Chaining technically work?
    (Mechanics, workflow, and components.)
  4. Why is Prompt Chaining better than prior solutions?
    (Comparison to alternatives, trade-offs.)
  5. How is Prompt Chaining used in real-world applications?
    (Use cases and industries where it’s applied.)

What is Prompt Chaining?
(Definition, purpose, and key characteristics.)

Definition

Prompt Chaining is a technique that connects multiple prompts together to create a structured, multi-step interaction with a large language model (LLM).

Purpose

It allows LLMs to maintain context across multiple turns, improving coherence and accuracy.

This method is particularly useful for complex tasks that require multiple reasoning steps, memory retention, or adaptive responses.

Key characteristics

  1. Stateful memory: Storing previous responses for future reference.
  2. Stepwise execution: Breaking complex tasks into smaller, sequential steps.
  3. Dynamic adaptation: Adjusting responses based on earlier inputs.

Characterist 1: Stateful memory

Stateful memory refers to an AI system’s ability to remember previous user inputs and responses.

Why is Stateful Memory useful for Prompt Chaining?

  • Without stateful memory, each new prompt is independent and forgets past interactions.
  • By maintaining memory, prompt chaining allows LLMs to carry over previous context, ensuring better continuity in responses.

Characterist 2: Stepwise execution

Stepwise execution means breaking a complex task into smaller, sequential steps.

Why does Stepwise Execution improve LLM interactions?

  • LLMs struggle with long, complex prompts that require multiple logical steps.
  • By breaking tasks down, prompt chaining allows LLMs to process information more effectively and generate step-by-step solutions.

Characterist 3: Dynamic adaptation

Dynamic adaptation means modifying the next prompt based on previous responses.

How does Dynamic Adaptation help in Prompt Chaining?

  • Instead of a rigid sequence, the AI can adjust its response strategy based on new information.
  • This is essential for interactive systems like chatbots, where the user’s response determines the next step.

What problem does Prompt Chaining solve?
(Pain points and why it’s needed.)

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How does Prompt Chaining technically work?
(Mechanics, workflow, and components.)


Why is Prompt Chaining better than prior solutions?
(Comparison to alternatives, trade-offs.)


How is Prompt Chaining used in real-world applications?
(Use cases and industries where it’s applied.)