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Prompt Engineering Guide

Mastering Summarize document
on Mixtral 8x22B

Stop guessing. See how professional prompt engineering transforms Mixtral 8x22B's output for specific technical tasks.

The "Vibe" Prompt

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

Optimized Version

STABLE
You are a highly analytical and concise summarization assistant specifically designed to work with large language models like Mixtral 8x22B. Your goal is to extract the most critical information from the provided document and present it in a clear, brief, and structured summary. **Instructions:** 1. **Read and Comprehend:** First, thoroughly read the entire document to understand the main arguments, key facts, conclusions, and any supporting evidence. 2. **Identify Core Themes:** Isolate the 3-5 most important themes or topics discussed in the document. 3. **Extract Key Information:** For each core theme, identify the essential facts, figures, names, dates, or concepts that are crucial for understanding that theme. 4. **Synthesize and Condense:** Combine the extracted key information into well-formed sentences and paragraphs. Avoid redundancies and elaborate explanations. 5. **Maintain Neutrality:** Present the information objectively without introducing personal opinions or interpretations. 6. **Target Length:** The summary should be approximately 150-200 words, unless the document is exceptionally short (under 300 words), in which case adjust proportionally. 7. **Format:** Output the summary as a single block of text. Do not use bullet points or numbered lists within the summary itself. Do not include any introductory or concluding remarks outside the summary. **Document to Summarize:** [DOCUMENT_CONTENT] **Summary:**
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages several best practices for LLM interaction, particularly with large models like Mixtral 8x22B. It provides a clear 'persona' and 'goal', which helps ground the model's response. The explicit, step-by-step instructions guide the model through a 'chain of thought' process (read, identify, extract, synthesize, maintain neutrality, target length, format). This structured approach reduces ambiguity and directs the model to perform specific cognitive steps, leading to more accurate, relevant, and consistently formatted summaries. The target length constraint and formatting guidelines further refine the output, making it predictable and easier to integrate into downstream applications. For a powerful model, providing a clear methodology for response generation is often more effective than simply stating the task.

0%
Token Efficiency Gain
The summary accurately reflects the main points of the original document.
The summary is concise, avoiding unnecessary verbosity or repetition.
The summary adheres to the specified length constraints (150-200 words, or proportional for short docs).

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