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Prompt

Explain how I could use chunking to run this prompt. Provide the actual prompts to use

Output

You asked how to use chunking to run a prompt that involves generating a large amount of information, such as creating 500 files in a structured format. Chunking is particularly useful when dealing with large tasks like this because it breaks down complex information into smaller, manageable parts, making it easier for both the model and you to process.

How to Use Chunking for Your Prompt

In your case, the task is to generate 500 files in a structured format. Instead of asking the model to generate all 500 files at once (which may be too large for the model to handle effectively), you can break the task into smaller chunks. For instance, you can divide the task into smaller groups of files—say 50 or 100 files at a time—and then combine the results afterward.

Here’s how you can apply chunking:

  1. Break Down the Task: Split the request into smaller groups. For example, ask for 50 files at a time.
  2. Use Consistent Structure: Maintain the same structure for each chunk so that the outputs are consistent and can be easily combined later.
  3. Iterate Over Chunks: After generating one chunk (e.g., 50 files), move on to the next until you reach 500.

Actual Prompts Using Chunking

Here are examples of prompts that chunk the original task into manageable parts:

Prompt 1: Generate the first 50 files

Imagine that everything you may wish to know about a person could be compacted into a single folder called `data`. We might imagine that the folder would have top-level subfolders called `professional` and `personal`.

This folder would consist of files which would record contextual information about that person's life.

For example, there might be a file called `hobbies`; one called `current job details`; another called `career aspirations`.

Your task is to come up with **the first 50 files** in this structure. Structure your output in file tree notation.

Prompt 2: Generate the next 50 files (files 51-100)

Now, continue from where we left off. Generate **the next set of 50 files**, starting from file number 51 up to file number 100, in the same structure as before.

Prompt 3: Continue with another chunk (files 101-150)

Continue generating files from where we left off. This time, create **the next set of 50 files**, starting from file number 101 up to file number 150, maintaining the same structure as before.

Prompt 4: Final chunk (files 451-500)

We are almost done! Now generate **the final set of 50 files**, starting from file number 451 up to file number 500, following the same structure as before.

Why This Works

  • Memory Efficiency: By breaking down your request into smaller chunks (e.g., groups of 50), you avoid overwhelming the model's memory or token limit.
  • Easier Debugging: If any errors occur in one chunk, it’s easier to correct them without having to regenerate everything.
  • Consistency: Since each chunk follows the same structure, combining them later is straightforward.

Technical Implementation

If you're using a tool like LangChain or similar frameworks for handling long texts or prompts, you can also automate this process using recursive or fixed-size chunking methods. For instance:

from langchain.text_splitter import RecursiveCharacterTextSplitter

text = "..." # your text
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size = 256,
    chunk_overlap = 20
)

docs = text_splitter.create_documents([text])

This code snippet demonstrates how you could split text into manageable chunks programmatically if you're working with large text inputs.

By using these techniques and prompts, you'll be able to efficiently generate and manage large outputs like your set of 500 files without running into memory or processing issues.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.