Prompt Engineering: Beyond the Basics

After conducting 17+ sessions on prompt engineering and working with LLMs daily, I’ve learned that effective prompting is less about “magic words” and more about understanding how these models process information.

The Three Pillars of Effective Prompting

1. Clarity and Structure

Your prompt should be unambiguous. LLMs are powerful, but they’re not mind readers.

Bad prompt:

Write code for the thing we discussed.

Good prompt:

Write a Python function that:
1. Accepts a list of integers
2. Filters out negative numbers
3. Returns the sum of remaining values
4. Includes error handling for empty lists
5. Has comprehensive docstrings

Use type hints and follow PEP 8 style guidelines.

2. Context and Constraints

Provide just enough context—not too much, not too little.

# Example: Building context for a code review
prompt = f"""
You are a senior Python developer reviewing code for production deployment.

Project context:
- Banking application (security-critical)
- Must follow PEP 8 and type hints required
- Performance is important (processes 10k+ transactions/day)

Review this code and identify:
1. Security vulnerabilities
2. Performance issues
3. Code quality concerns

Code to review:
{code_snippet}
"""

3. Output Format Specification

Always specify the exact format you want:

prompt = """
Analyze the following text and return a JSON object with this exact structure:

{
  "sentiment": "positive" | "negative" | "neutral",
  "confidence": 0.0 to 1.0,
  "key_phrases": [string array],
  "entities": [
    {"text": string, "type": "person" | "organization" | "location"}
  ]
}

Text: {user_input}
"""

Advanced Techniques

Chain-of-Thought Reasoning

For complex problems, ask the model to think step-by-step:

Solve this problem using chain-of-thought reasoning:

Problem: Calculate the optimal batch size for processing 100,000 records
where each API call can handle 500 records but has rate limiting of
10 requests per second.

Think through this step by step:
1. First, calculate total batches needed
2. Then, determine time constraints from rate limiting
3. Finally, recommend optimal batch size and processing strategy

Show your work for each step.

Few-Shot Learning

Provide examples to guide the model’s behavior:

Extract action items from meeting notes.

Example 1:
Input: "John will prepare the Q4 report by Friday. Sarah mentioned
she'll review the marketing proposal."
Output:
- [ ] John: Prepare Q4 report (Due: Friday)
- [ ] Sarah: Review marketing proposal

Example 2:
Input: "We need to schedule a follow-up meeting next week. Mike
volunteered to create the agenda."
Output:
- [ ] Team: Schedule follow-up meeting (Due: Next week)
- [ ] Mike: Create agenda for meeting

Now process this:
{meeting_notes}

Role-Based Prompting

Frame the model’s perspective:

You are an experienced DevOps engineer who specializes in Kubernetes
deployments. You prioritize:
1. Security best practices
2. Resource efficiency
3. Observability
4. Disaster recovery

Given this context, review the following deployment YAML and suggest
improvements with explanations.

Common Pitfalls to Avoid

1. Ambiguous Instructions

❌ “Make this better” ✅ “Improve code readability by: adding descriptive variable names, breaking long functions into smaller ones, and adding type hints”

2. Ignoring Token Limits

Always be aware of context windows:

def build_prompt(context: str, max_context_tokens: int = 3000):
    if count_tokens(context) > max_context_tokens:
        # Truncate or summarize
        context = smart_truncate(context, max_context_tokens)
    return build_final_prompt(context)

3. Not Handling Edge Cases

Test your prompts with:

• Empty inputs
• Very long inputs
• Malformed data
• Adversarial inputs

Measuring Prompt Effectiveness

Track these metrics:

class PromptMetrics:
    def __init__(self):
        self.success_rate = 0.0
        self.avg_tokens_used = 0
        self.avg_response_time = 0.0
        self.format_compliance = 0.0
        
    def evaluate_prompt(self, test_cases):
        results = []
        for test in test_cases:
            response = llm.generate(test.prompt)
            results.append({
                'success': self.is_correct(response, test.expected),
                'tokens': count_tokens(response),
                'time': measure_time(response),
                'format_valid': validate_format(response)
            })
        return self.aggregate_metrics(results)

Prompt Templates for Common Tasks

Code Generation

Language: {language}
Task: {description}
Requirements:
- {requirement_1}
- {requirement_2}
Style: {style_guide}
Include: error handling, logging, and tests

Text Summarization

Summarize the following text in {word_count} words or less.
Focus on: {key_aspects}
Audience: {target_audience}
Tone: {desired_tone}

Text: {content}

Data Extraction

Extract structured data from this text and return as JSON.

Schema:
{json_schema}

Rules:
- Use null for missing fields
- Validate dates are in ISO format
- Convert numbers to appropriate types

Text: {source_text}

Conclusion

Effective prompt engineering is a skill that improves with practice. Start with clear, structured prompts, iterate based on results, and build a library of patterns that work for your use cases.

Remember: the best prompt is one that consistently produces the results you need, regardless of how elegant or clever it seems.

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Have questions about prompt engineering? Let’s discuss your specific use cases.