Breaking Down Complex Tasks into Manageable Subtasks

When working with language models, giving them a large, intricate task all at once can overwhelm the model. Instead, breaking down the task into smaller, more focused prompts allows the model to handle each piece individually, improving clarity and minimizing errors.

Example:
Instead of asking the model to summarize a lengthy document in one go, break it down into manageable sections. For example:

• “Summarize Section 1 of the document.”

• “Summarize Section 2 of the document.”

• “Summarize Section 3 of the document.”

This segmentation improves the quality and accuracy of the response because the model can focus on smaller chunks of information, ensuring it produces more relevant and precise summaries.

Classifying Queries for Better Structure

When a task involves multiple instructions or cases, classifying them based on their type helps you craft more organized and effective prompts. By breaking a complex task into a series of independent steps, you help the model understand the task more clearly, reducing the chances of misinterpretation.

Example:
If you need to analyze several calculations, instead of asking for a general answer, break it down into individual steps:

• “Analyze Calculation 1.”

• “Analyze Calculation 2.”

• “Analyze Calculation 3.”

This structured approach ensures that the model can handle each case separately and provides more accurate insights.

Benefits of Sequential Prompting

A key advantage of using a sequential approach is the reduction of error rates. By structuring the task step-by-step, you make it easier for the model to understand and execute each part correctly. This minimizes the risk of errors or misinterpretation.

Additionally, each model has a fixed context length or token limit, meaning it can only process a certain amount of information at a time. By breaking tasks into smaller sections, you stay within these limits while maintaining focus and improving the model’s performance.

Example of Chunking to Improve Focus

When summarizing a lengthy document, instead of asking for a summary of the entire document, you can use a chunking strategy:

1. “Summarize the Introduction of the document.”

2. “Summarize the Body of the document.”

3. “Summarize the Conclusion of the document.”

After summarizing each section, combine them into a comprehensive summary. This method enhances the model’s focus and allows it to process smaller, more digestible parts of the text.

Running Summaries for Context

In cases where tasks require referencing earlier sections or previous discussions, a running summary can be useful. This approach helps the model maintain context across multiple stages of the task.

For instance, after summarizing a section, create a running summary to provide context for subsequent sections:

1. "Summarize Section 1 and create a running summary."

2. "Summarize Section 2, using the running summary of Section 1."

3. "Summarize Section 3, with context from Sections 1 and 2."

This ensures that the responses are coherent and connected throughout the task.

Conversational Prompts and Token Limits

When engaging in extended conversations or tasks with the model, you may reach its token limit, which restricts the amount of text the model can process in a single prompt. To continue the conversation, generate a summary of the prior interactions and feed it into the next prompt. This helps maintain continuity while staying within the model’s token limits.

Example:
If the conversation exceeds the token limit, generate a concise summary and use that to prompt the next part of the conversation.

Enhancing Depth with Step-by-Step Analysis

Sometimes, you may need deeper analysis than a straightforward answer can provide. For example, instead of simply asking if a calculation is correct, prompt the model to analyze the calculation step-by-step. This will give you a more thorough understanding of the reasoning process behind the answer.

Example:
Rather than asking, “Is this solution correct?” ask:

• “Analyze each step of the following calculation to determine its accuracy.”

This strategy provides more valuable insights and helps you evaluate the logic behind the solution.

Specifying Response Format for Integration

For tasks that involve further processing, such as integration into applications or systems, specifying the format for the model's response can streamline its usability. For example, you can ask the model to provide its response in a certain format, like using triple quotation marks for the main response and triple stars for additional details.

Example:

• “Provide the main response in triple quotation marks: 'Main Response.' Use triple stars for additional context: Additional Information.”

This structure makes it easier to extract and use the information generated by the model in downstream tasks.

Evaluating Model Outputs with Gold Standard Answers

To ensure the accuracy of model responses, especially in tasks requiring factual correctness, it’s effective to compare the model's output with a gold standard—a known correct answer. This helps measure the factual accuracy of the generated text.

Example:
If a question requires certain facts, prompt the model to verify how many of these facts are included in its response:

• “How many of the following facts are present in the answer?”

  1. Fact A

  2. Fact B

  3. Fact C

This evaluation ensures that the output aligns with expected information and maintains factual correctness.

Activities

Activity 1: Chunking for Summarization

• Take a long article or document and break it into sections. Write individual prompts to summarize each section and then combine those summaries into a comprehensive overview. Evaluate the effectiveness of chunking versus summarizing the entire document in one prompt.

Activity 2: Step-by-Step Calculation Analysis

• Choose a mathematical or logical problem. Instead of asking the model to simply check if the solution is correct, prompt it to analyze each step involved. Compare the insights generated through this detailed approach with a single-step query to assess the depth of understanding.