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Prompt Engineering Guide

Mastering Analyze sentiment
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

"What's the sentiment of this text: "I absolutely love this new phone! The camera is incredible and the battery life is amazing. Highly recommend it.""
Low specificity, inconsistent output

Optimized Version

STABLE
You are Llama 3.1 8B, an expert in natural language processing and sentiment analysis. Your task is to determine the sentiment (Positive, Negative, or Neutral) of the provided text. Follow these steps for analysis: 1. **Identify Key Sentiment-Bearing Words/Phrases**: Extract words or phrases that directly indicate positive, negative, or neutral sentiment. 2. **Evaluate Context and Modifiers**: Consider how adverbs (e.g., 'very', 'not'), adjectives, and sentence structure influence the core sentiment. 3. **Aggregate Sentiment**: Combine the individual sentiment indicators considering their weight and context to form an overall sentiment. 4. **State Final Sentiment**: Conclude with the overall sentiment (Positive, Negative, or Neutral). Text: "I absolutely love this new phone! The camera is incredible and the battery life is amazing. Highly recommend it." Chain of Thought:
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt uses a Chain-of-Thought (CoT) approach, guiding the model through a structured thinking process. It explicitly defines the persona ('expert in natural language processing'), the task, and detailed steps. This not only forces the model to break down the task but also helps in identifying and weighing sentiment-bearing terms, considering modifiers, and then aggregating its findings. This structured approach reduces ambiguity and the likelihood of surface-level analysis, leading to more accurate and consistent sentiment detection, especially for nuanced or complex texts. It also sets up a clear expectation for the output format (CoT followed by final sentiment).

0%
Token Efficiency Gain
The optimized_prompt should consistently output 'Positive' for the given text.
The optimized_prompt should provide a brief explanation or chain of thought before the final sentiment.
The naive prompt should also correctly identify 'Positive' sentiment for this straightforward example.

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