Advanced Prompt Engineering Tips for Token Efficiency

Prompt engineering is both an art and a science. The way you structure your prompts can dramatically impact both the quality of outputs and the number of tokens consumed. In this guide, we'll explore advanced techniques to master both aspects.

1. Use Token-Efficient Formats

Structured formats consume fewer tokens while providing clarity. JSON, YAML, and markdown are token-efficient choices.

❌ Inefficient:

"Please analyze this customer review: The product is great but the shipping was slow. I would buy again but I'm concerned about delivery times."

✅ Efficient:

Review: "Product great, slow shipping. Would repurchase but concerned about delivery."
Format: Analyze sentiment and concerns.

2. Leverage Few-Shot Examples Wisely

Examples are powerful but expensive. Use 1-2 high-quality examples instead of many mediocre ones.

Good Few-Shot Example:

Input: "The movie was fantastic!"
Output: {"sentiment": "positive", "confidence": 0.95}

Input: "I hated it."
Output: {"sentiment": "negative", "confidence": 0.90}

3. Chain-of-Thought Optimization

Chain-of-thought prompting improves reasoning but adds tokens. Use it only when needed.

Simple tasks: Skip chain-of-thought to save tokens
Complex reasoning: Use abbreviated chain-of-thought
Critical decisions: Full chain-of-thought justified

4. Dynamic Prompt Construction

Build prompts dynamically based on input complexity. Simple inputs get simpler prompts.

Strategy:

Detect input complexity
Adjust system prompt length accordingly
Reduce examples for simple queries
Add context only when needed

5. Compression Techniques

Modern language models can understand compressed prompts:

Before (longer):

"You are an expert data analyst. Please analyze the following dataset and provide insights about trends, patterns, and anomalies."

After (compressed):

"Analyze dataset: trends, patterns, anomalies"

6. Context Window Management

With context windows getting larger, prioritize what goes in:

Essential: Core task definition
Important: Critical examples and context
Nice to have: Background information
Low priority: Explanations and disclaimers

7. Token-Aware System Messages

System messages are crucial but count toward token limits. Make them concise:

❌ Long (150 tokens):

"You are a helpful assistant that specializes in providing detailed and thorough responses. You should always consider multiple perspectives..."

✅ Short (20 tokens):

"Assistant: Concise, accurate responses."

Real-World Implementation

Here's a practical prompt that's both efficient and effective:

System: You classify sentiment. Be brief.

User: Review: "Love the product! Shipping was slow."
Output format: {sentiment: string, score: 0-1}

Task: Classify the review.

Conclusion

Effective prompt engineering combines clarity, conciseness, and strategic use of examples. By implementing these techniques, you can significantly reduce token consumption while maintaining (or even improving) output quality.

The key is experimentation. Test your prompts, measure token usage, and iterate continuously.