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Prompt Engineering Guide

Mastering Summarize document
on Llama 3.1 70B

Stop guessing. See how professional prompt engineering transforms Llama 3.1 70B's output for specific technical tasks.

The "Vibe" Prompt

"Summarize the following document: [document_text]"
Low specificity, inconsistent output

Optimized Version

STABLE
You are Llama 3.1 70B, an advanced AI designed for complex text analysis. Your task is to provide a concise, accurate, and comprehensive summary of the provided document. Follow these steps to produce the optimal summary: 1. **Identify Key Entities and Concepts:** Go through the document and list all major entities (people, organizations, locations, products, events) and key concepts/themes discussed. Extract them as a bulleted list initially. 2. **Determine the Main Argument/Purpose:** What is the overarching message or primary goal of this document? Is it informational, persuasive, narrative, analytical, etc.? State this clearly. 3. **Extract Supporting Details:** For each key concept or argument, identify the most crucial supporting facts, data, or examples presented. Keep these brief and focused. 4. **Synthesize into a Draft Summary:** Combine the main argument, key entities, and supporting details into a coherent draft paragraph. Ensure logical flow and avoid redundancy. 5. **Refine for Conciseness and Clarity:** Review the draft. Remove any extraneous words or phrases. Ensure the language is clear, objective, and accurately reflects the original document's content. Focus on conveying the maximum information with the fewest words. 6. **Final Summary:** Present the refined, concise summary. Document to summarize: [document_text]
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages chain-of-thought prompting, explicitly outlining a step-by-step process for the model. This guides the model to perform a deeper analysis before generating the summary. By breaking down the task into smaller, manageable steps (identifying entities, main argument, supporting details, drafting, and refining), it reduces the cognitive load and increases the likelihood of a higher-quality, more accurate, and more comprehensive summary. The explicit instruction to 'Refine for Conciseness and Clarity' directly addresses a common summarization challenge and encourages more efficient token usage in the final output by prioritizing essential information.

%
Token Efficiency Gain
The optimized prompt's summary should be more comprehensive, covering more key aspects of the document than the naive version.
The optimized prompt's summary should demonstrate better logical flow and coherence.
The optimized prompt's summary should be more accurate, making fewer factual errors or misinterpretations.

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