Prompt Engineering Guide
Mastering Analyze sentiment
on DeepSeek V3
Stop guessing. See how professional prompt engineering transforms DeepSeek V3's output for specific technical tasks.
The "Vibe" Prompt
"Tell me what's the vibe of this text: [TEXT]"
Low specificity, inconsistent output
Optimized Version
You are a sentiment analysis AI. Your task is to determine the sentiment (positive, negative, or neutral) of the provided text. Follow these steps:
1. Identify key words, phrases, and their emotional connotations.
2. Consider the overall context and any modifiers (e.g., 'not', 'very').
3. Synthesize this information to arrive at a sentiment.
4. Output only the sentiment label.
Text: [TEXT]
Thought Process:
Structured, task-focused, reduced hallucinations
Engineering Rationale
The optimized prompt provides clear instructions, defines the task precisely, and utilizes a chain-of-thought (CoT) approach. This CoT guides the model through the analysis process, improving accuracy and consistency. It asks for specific steps and a final, clean output, reducing ambiguity and hallucination. Telling the model to 'Output only' the sentiment label streamlines the response, making it easier to parse programmatically.
0%
Token Efficiency Gain
The optimized_prompt should lead to more accurate sentiment classifications for nuanced texts.
The optimized_prompt should consistently output one of the three sentiment labels (positive, negative, neutral).
The optimized_prompt should provide a brief 'Thought Process' before the final sentiment, demonstrating its reasoning.
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