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Advanced Prompt Engineering: Beyond Basic Templates

Deep dive into prompt optimization strategies for complex reasoning tasks and multi-step workflows.

January 5, 2025
15 min read
Prompt EngineeringLLMOptimization

Beyond Basic Templates

Prompt engineering has evolved from simple template filling to a sophisticated discipline. Advanced techniques can dramatically improve LLM performance on complex reasoning tasks and multi-step workflows.

Chain-of-Thought Prompting

Encourage step-by-step reasoning by explicitly asking the model to show its work:

prompt = """
Solve this problem step by step:

Problem: {problem}

Let's think through this:
1. First, I need to...
2. Then, I should...
3. Finally, the answer is...
"""

This technique improves performance on mathematical, logical, and multi-step reasoning tasks.

Few-Shot Learning

Provide examples in your prompt to guide the model's behavior:

prompt = """
Here are examples of good responses:

Example 1:
Input: "What is machine learning?"
Output: "Machine learning is a subset of artificial intelligence..."

Example 2:
Input: "Explain neural networks"
Output: "Neural networks are computing systems..."

Now answer:
Input: "{user_query}"
Output:
"""

Role-Based Prompting

Assign a role to the model to shape its responses:

prompt = """
You are an expert software architect with 20 years of experience.
Your task is to design scalable systems.

Question: {question}
"""

Output Formatting

Specify the desired output format explicitly:

prompt = """
Analyze this code and provide:
1. A brief summary (2-3 sentences)
2. Three potential issues
3. Suggested improvements

Format your response as JSON:
{
  "summary": "...",
  "issues": ["...", "...", "..."],
  "improvements": ["...", "...", "..."]
}
"""

Prompt Chaining

Break complex tasks into multiple prompts:

  1. First prompt: Analyze and break down the problem
  2. Second prompt: Solve each sub-problem
  3. Third prompt: Synthesize the solution

Self-Consistency

Generate multiple responses and select the most consistent answer. This improves reliability on reasoning tasks.

Prompt Optimization

Iterative Refinement

Test different prompt variations and measure performance. Keep what works, discard what doesn't.

Parameter Tuning

Adjust temperature, top-p, and other parameters based on task requirements:

  • Low temperature (0.1-0.3): More deterministic, better for factual tasks
  • High temperature (0.7-1.0): More creative, better for generation tasks

Common Pitfalls

  • Overly verbose prompts that confuse the model
  • Ambiguous instructions
  • Inconsistent formatting
  • Ignoring context window limits

Best Practices

  1. Be explicit and specific
  2. Use clear structure and formatting
  3. Provide examples when possible
  4. Test and iterate
  5. Document your prompts

Conclusion

Advanced prompt engineering techniques can unlock significant performance improvements. By understanding these methods and applying them thoughtfully, you can build more reliable and capable LLM applications.

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