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Prompt Engineering Guide

Mastering Summarize document
on Llama 3.1 8B

Stop guessing. See how professional prompt engineering transforms Llama 3.1 8B's output for specific technical tasks.

The "Vibe" Prompt

"Summarize the following document: {document}"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert summarization AI, trained to extract key information and present it concisely. Your task is to summarize the provided document, focusing on the main arguments, conclusions, and any significant data or findings. Follow a chain-of-thought process to ensure accuracy and completeness. <document> {document} </document> <thinking> 1. **Identify the main topic/purpose:** What is this document primarily about? 2. **Extract key arguments/points:** What are the central claims or pieces of information presented? 3. **Note supporting evidence/data:** Are there any statistics, examples, or findings that back up the arguments? 4. **Identify conclusions/outcomes:** What are the ultimate takeaways or results? 5. **Synthesize into concise paragraphs:** Combine the identified information into a coherent and brief summary. </thinking> <summary_guidelines> - Be concise and articulate. - Focus on the most critical information. - Avoid jargon where simpler terms suffice. - Maintain the original meaning and tone. - Ensure the summary is no more than 20% of the original document length. </summary_guidelines> Based on the above document and my chain-of-thought, provide the summary:
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages several best practices for LLMs. It starts by defining a clear 'persona' ('expert summarization AI'). It then explicitly outlines a 'chain-of-thought' process, guiding the model through the steps required for a good summary (identification, extraction, synthesis). This reduces hallucination and improves focus. It also uses XML-like tags (<document>, <thinking>, <summary_guidelines>) to structure the input clearly, making it easier for the model to parse different sections. Finally, it provides explicit 'summary_guidelines' to define desired output characteristics like conciseness and length constraints. The 'vibe_prompt' is too vague and lacks direction, potentially leading to less focused or less complete summaries.

0%
Token Efficiency Gain
The optimized prompt explicitly asks for a chain-of-thought process.
The optimized prompt defines a persona for the AI.
The optimized prompt uses XML-like tags to delineate sections.

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