Mastering Complex Tasks with Prompt Engineering

Learn a powerful technique to overcome limitations and achieve impressive results with generative AI by strategically dividing complex tasks into manageable sub-prompts.

Generative AI models are incredibly powerful, capable of generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way. However, even the most advanced models can struggle with truly complex tasks that require multi-step reasoning or diverse knowledge integration. This is where the art of sub-prompting comes into play.

What is Sub-Prompting?

Sub-prompting is a technique where you break down a large, complex prompt into smaller, more manageable sub-prompts. Each sub-prompt focuses on a specific aspect or stage of the overall task. The results from each sub-prompt are then combined or used as input for subsequent sub-prompts, ultimately leading to a complete solution.

Why is Sub-Prompting Important?

• Overcomes Context Limits: Large language models have limitations on the amount of context they can process in a single prompt. Sub-prompting allows you to work around these limitations by processing information in chunks.
• Improves Accuracy and Relevance: By focusing on specific subtasks, sub-prompts guide the model towards more accurate and relevant results. Each step refines the output, leading to a higher quality final outcome.
• Enables Complex Reasoning: Sub-prompting allows you to implement complex reasoning chains. You can essentially “teach” the model how to think through a problem step by step.

Breaking Down a Task with Sub-Prompts: A Step-by-Step Guide

Let’s illustrate this concept with an example. Imagine you want to generate a creative short story about a robot who discovers love. This is a complex task that requires multiple elements: character development, plot structure, emotional depth, and creative writing style.

Here’s how sub-prompting could help:

1. Sub-Prompt 1: “Describe the physical appearance and personality traits of a robot designed for domestic tasks.”
2. Sub-Prompt 2 (using the output from Sub-Prompt 1): “Imagine this robot encounters a human child who is lonely and needs a friend. How does the robot react?”

3. Sub-Prompt 3 (building on the previous responses): “Over time, the robot develops unexpected feelings for the child. Describe these feelings in detail.”

4. Sub-Prompt 4: “Write a short story scene where the robot expresses its love for the child through actions rather than words.”

5. Sub-Prompt 5: “Expand this scene into a complete short story, focusing on the unique challenges and triumphs of their relationship.”

Code Example (Illustrative):

While sub-prompting doesn’t involve specific code syntax, you can use programming languages like Python to automate the process. Here’s a simplified example:

import openai

def generate_story(sub_prompts):
  story = ""
  for prompt in sub_prompts:
    response = openai.Completion.create(engine="text-davinci-003", 
                                     prompt=prompt,
                                     max_tokens=150)
    story += response.choices[0].text + "\n"
  return story

sub_prompts = [
  "Describe the physical appearance and personality traits of a robot designed for domestic tasks.",
 #... (Add remaining sub-prompts as described above) 
]

final_story = generate_story(sub_prompts)
print(final_story)

Key Takeaways:

• Sub-prompting is a powerful technique to overcome limitations and achieve impressive results with generative AI.
• Break down complex tasks into smaller, manageable sub-tasks.
• Use the output of each sub-prompt as input for subsequent steps.
• Experiment with different sub-prompt structures to find what works best for your task.

Sub-prompting opens up a world of possibilities, allowing you to leverage the full potential of generative AI for complex and creative endeavors.