Advanced Prompt Engineering Techniques That Actually Work

Prompt engineering is both an art and a science. After working with dozens of clients and thousands of prompts, we've identified the techniques that consistently deliver better results.

The Foundation: Clear Instructions

Before diving into advanced techniques, nail the basics:

Bad: "Write about AI"
Good: "Write a 500-word blog post introducing AI to small business owners,
focusing on practical applications and avoiding technical jargon."

Technique 1: Chain of Thought (CoT)

Ask the model to think step-by-step:

Solve this problem step by step:
1. First, identify the key information
2. Then, determine what we're trying to find
3. Finally, calculate the answer

Problem: [Your problem here]

When to use: Complex reasoning, math problems, multi-step tasks

Technique 2: Few-Shot Learning

Show examples of what you want:

Convert customer feedback to structured data:

Example 1:
Input: "Love the product but shipping was slow"
Output: {"sentiment": "positive", "issue": "shipping_speed"}

Example 2:
Input: "Great customer service, solved my problem quickly"
Output: {"sentiment": "positive", "issue": null}

Now convert this:
Input: [New feedback]

When to use: Specific formatting, domain-specific tasks, edge cases

Technique 3: Role-Playing

Give the model a persona:

You are a senior software architect with 15 years of experience
in distributed systems. Review this code and provide feedback
focusing on scalability and maintainability:

[Code here]

When to use: Need specific expertise, tone, or perspective

Technique 4: Constrained Generation

Set clear boundaries:

Generate product descriptions following these rules:
- Exactly 50 words
- Include one benefit, one feature, one call-to-action
- Use active voice
- Avoid superlatives
- Target audience: small business owners

When to use: Strict requirements, compliance, consistency

Technique 5: Self-Critique

Ask the model to check its work:

First, write a summary of this article.
Then, critique your own summary:
- Is it accurate?
- Is it concise?
- Does it capture the main points?
Finally, provide a revised summary addressing any issues you found.

When to use: High-stakes outputs, complex tasks, quality assurance

Technique 6: Decomposition

Break complex tasks into steps:

# Task: Create a marketing campaign

Step 1: Analyze the target audience
[Let the model complete]

Step 2: Based on the audience analysis, identify 3 key messages
[Let the model complete]

Step 3: For each message, create a social media post
[Let the model complete]

When to use: Complex projects, when you need control over the process

Anti-Patterns to Avoid

1. Being Too Vague

❌ "Make it better" ✅ "Rewrite this focusing on clarity and reducing word count by 30%"

2. Overloading Context

❌ Dumping your entire documentation ✅ Providing relevant excerpts with clear structure

3. Assuming Knowledge

❌ "Use the standard approach" ✅ "Use JSON format with snake_case keys"

4. Ignoring Model Limitations

❌ Expecting perfect accuracy on factual questions without verification ✅ Using RAG or citations for fact-based responses

Measuring Prompt Quality

Track these metrics:

• Success rate: How often does it work first try?
• Consistency: Similar inputs → similar outputs?
• Efficiency: Token usage vs. output quality
• Error rate: How often do you need to retry?

Production Tips

Version Your Prompts

const PROMPTS = {
  v1: "Summarize this article",
  v2: "Summarize this article in 3 bullet points",
  v3: "Create a 3-bullet summary focusing on key takeaways",
};

// Easy A/B testing
const prompt = PROMPTS.v3;

Add Guardrails

Generate a product description.

Constraints:
- Do not mention competitors
- Do not make health claims
- Do not use superlatives like "best" or "perfect"
- If you cannot generate appropriate content, return "CONTENT_POLICY_VIOLATION"

Temperature Matters

• Temperature 0: Consistent, deterministic outputs (data extraction)
• Temperature 0.7: Balanced creativity (content writing)
• Temperature 1+: Maximum creativity (brainstorming)

The Iteration Loop

1. Write your initial prompt
2. Test with diverse inputs
3. Analyze failures and edge cases
4. Refine the prompt
5. Repeat until success rate is acceptable

Conclusion

Great prompts are specific, provide context, show examples, and set clear expectations. The best prompt engineers iterate constantly and measure everything.

Need help optimizing your prompts for production? Reach out to our team.