The aim of Prompt Chaining is to decompose multi-step tasks into smaller, more manageable steps such that the LLM can handle the goal more effectively. It focuses on breaking down the problem and feeding each step’s output to the next step in the chain.
Both Prompt Chaining and Chain-of-thought prompting enhance large language models’ performance on complex tasks. Prompt Chaining breaks down tasks into manageable steps, while chain-of-thought prompting guides the LLM’s reasoning, making results more transparent. Prompt Chaining is better for sequential tasks like code generation, while chain-of-thought prompting excels at logical reasoning tasks like scientific problem solving. Choosing the right technique depends on the specific task and desired outcome.
Example of Prompt Chaining (Courtesy of Google Bard using Gemini Pro)
Generating a poem about a cat
Step 1: Generate animal description:
Prompt: Write a descriptive paragraph about a cat.
Output: A sleek black cat with emerald eyes crouched silently, its tail flicking back and forth in anticipation.
Step 2: Generate details about the setting:
Prompt: Based on the previous description, write a sentence about the setting where the cat is.
Output: The cat sat on the edge of a cobblestone alleyway, bathed in the soft glow of moonlight.
Step 3: Generate action:
Prompt: Based on the previous description, write a sentence about what the cat does next.
Output: Suddenly, the cat sprang into the air, chasing a flurry of fluttering leaves.
Step 4: Generate emotion:
Prompt: Based on the previous description, write a sentence about what the cat might be feeling.
Output: A playful glint filled the cat’s eyes as it darted between the leaves, its joy echoing in the silent night.
Step 5: Generate final poem:
Prompt: Write a poem about a cat based on the previous descriptions.
Output:
A shadow in the moonlight’s gleam,
Emerald eyes that brightly beam.
A sleek black form, a playful soul,
Chasing leaves that take their toll.
This example demonstrates how Prompt Chaining can be used to break down a complex task (generating a poem) into smaller, more manageable steps. Each step builds upon the previous one, ultimately leading to the desired outcome.
See Also: Chain-of-thought Reasoning