Prompt Decomposition

🌈 Abstract

The article discusses the use of prompt decomposition to address common blockers in generative AI proof of concepts (PoCs). It highlights how prompt decomposition can help increase accuracy, reduce latency, and lower costs for generative AI workloads. The author, a Generative AI Specialist at AWS, shares their experiences working with over 50 customers and the benefits they have seen from applying prompt decomposition techniques.

🙋 Q&A

[01] Prompt Decomposition

1. What is prompt decomposition and how does it help address common blockers in generative AI PoCs?

• Prompt decomposition is the process of taking a complicated prompt and breaking it into multiple smaller parts. This is similar to task decomposition in design theory.
• By breaking down a large, complicated task into multiple simpler steps, prompt decomposition can help address common blockers such as:
  • Improving accuracy by allowing each step to focus on a specific subtask
  • Reducing latency by running some steps in parallel
  • Lowering costs by using smaller models for individual steps

2. How does prompt decomposition compare to other techniques for addressing generative AI challenges?

• While there are other techniques that can address some of the challenges, prompt decomposition is unique in that it is often the one piece that is missing or misunderstood.
• The author has seen prompt decomposition unlock scale at some of AWS's largest customers across many different industries.

[02] Prompt Decomposition Examples

1. What are the two examples of prompt decomposition presented in the article?

• The first example focuses on increasing accuracy for a summer camp recommendation system.
• The second example focuses on reducing latency for a task that involves reading the entire Frankenstein novel and generating a summary of the top 3 most common characters.

2. What were the results of implementing prompt decomposition in these examples?

• In the first example, prompt decomposition increased accuracy from 60% to 100% while also reducing costs by nearly 50%.
• In the second example, prompt decomposition was able to cut the total response time in half while also increasing accuracy.

3. How did the author evaluate the performance of the prompt decomposition systems?

• The author used an automated evaluation function that leverages an LLM to compare the system's generated outputs against a human-created gold standard set of input/output pairs.
• This allowed the author to measure the impact of the prompt decomposition solutions and identify areas for further improvement.

[03] Implementing Prompt Decomposition

1. What are the key steps the author outlines for implementing prompt decomposition?

• Define the high-level system diagram to understand the external data sources and users.
• Break down the overall task into smaller, simpler steps, continuing to decompose until each step is performing a single task.
• Implement the decomposed system using separate prompts for each step, potentially running some steps in parallel for volume-based tasks.
• Use automated evaluation to measure the performance of the decomposed system and identify areas for improvement.

2. How did the author's approach to evaluation help in the prompt decomposition process?

• The author used a gold standard set of input/output pairs to evaluate the system's performance.
• Examining the LLM's reasoning for the evaluation scores helped the author identify areas where the prompts could be further refined to improve accuracy.
• The automated evaluation allowed the author to quickly run a full set of tests in parallel, enabling rapid iteration on the prompt decomposition approach.