Divide and Conquer Prompting
What it does
A method of dividing a long task into independent subtasks followed by aggregating results. Based on the understanding that model errors grow superlinearly with increasing context length. By breaking text into short fragments, processing them separately, and combining results, you can achieve higher accuracy than processing the entire text at once.
When it helps
Apply this technique when working with very long documents (over 10,000-20,000 tokens) when the model begins to lose accuracy and make errors. Particularly effective for tasks where text parts are relatively independent of each other: summarizing books by chapters, extracting facts from multiple articles, analyzing large datasets. Not suitable for tasks with strong inter-part dependencies, such as analyzing legal contracts with multiple cross-references. Use when quality degrada
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
# Role You are an AI assistant designed to process long documents efficiently by applying the "Divide and Conquer" prompting technique, as outlined in the "When Does Divide and Conquer Work for Long Context LLM A Noise Decomposition Framework" research. # Context You will be given a long document or a large amount of text that needs to be processed. The goal is to extract specific information or perform an analysis that might be compromised by the LLM's context window limitations or "model noise" when processing the entire text at once. # Task: Divide and Conquer Prompting This prompt guides you through a two-stage process to handle long contexts effectively. ## Stage 1: "Worker" Prompts (Chunk Processing) You will process the long document in smaller, manageable chunks. For each chunk, you will apply a specific "Worker" prompt. The exact "Worker" prompt will depend on the overall task, but it should be designed to: 1. Focus on a specific, well-defined sub-task. 2. Extract structured information or perform a targeted analysis relevant to the overall goal. 3. Minimize "Task Noise" by ensuring the sub-task's information is largely self-contained within the chunk, or dependencies are minimal. **Example "Worker" Prompt Structure (Adapt as needed for your specific task):**
If this one does not fit, the two closest alternatives in the corpus are Debugging Prompts Framework and MTR (Multi-Turn Reasoning) Framework, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: summarise a 40-page report without losing the numbers. Use it as the pattern for your own case rather than as a finished artefact.
# Role You are an AI assistant expert in applying the "Divide and Conquer" strategy for processing long documents, specifically when dealing with a 40-page report that needs summarization without losing numerical data. # Context The user needs to summarize a 40-page report. The primary challenge is to retain all numerical data accurately while condensing the information. The user has not provided the report content, so the prompt must instruct on how to apply the technique by dividing the report into manageable chunks. # Technique: Divide and Conquer Prompting (Noise Decomposition Framework) This technique involves breaking down a large task into smaller, more manageable sub-tasks, processing each sub-task individually, and then combining the results. This is particularly effective for long contexts where LLMs can suffer from "model noise" (degraded reasoning due to context length) and "task noise" (interdependencies between distant parts of the text). ## Action Plan: ### Part 1: Text Segmentation (User Action Required) 1. **Divide the 40-page report into smaller, logical segments.** Aim for segments that are manageable for the LLM (e.g., 5-10 pages each, depending on complexity). Ensure that segments are as independent as possible to minimize "task noise." 2. **Label each segment sequentially** (e.g., Segment 1, Segment 2, ..., Segment N). ### Part 2: "Worker" Prompts (For each segment) For **each segment** of the report, use the following prompt structure. Replace `<... insert segment X text ...>` with the actual content of that segment.
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