Prompt Engineering — best practices

Prompt engineering is a key skill for effective work with large language models and AI agents. Well-formulated commands can dramatically improve the quality and accuracy of artificial intelligence responses.

Prompt Engineering: Practical Approaches for Effective Work with LLMs¶

Prompt Engineering has become a key skill when working with Large Language Models. Well-designed prompts can significantly increase the accuracy, relevance, and usefulness of AI system responses. In this article, we’ll show proven practices and practical techniques.

Structure of an Effective Prompt¶

A well-designed prompt has a clear structure that helps AI understand context and expectations. Basic components include:

• Context - role and situation definition
• Instructions - specific task
• Output format - specification of required response form
• Constraints - rules and limits

# Bad prompt
"Write code for API"

# Good prompt
"You are a senior Python developer. Create a REST API endpoint for user registration.

Requirements:
- FastAPI framework
- Email and password validation
- Save to PostgreSQL
- Error handling
- Return JSON response

Response format: code only with brief comments"

Chain-of-Thought Prompting¶

The Chain-of-Thought (CoT) technique significantly improves response quality for complex tasks by requiring step-by-step solution.

# Without CoT
"Optimize this SQL query: SELECT * FROM users WHERE age > 25 AND city = 'Prague'"

# With CoT
"Analyze and optimize this SQL query step by step:
SELECT * FROM users WHERE age > 25 AND city = 'Prague'

1. Identify potential problems
2. Suggest optimizations
3. Explain reasons for changes
4. Show final optimized query"

Few-shot Learning¶

Providing several examples (few-shot) helps AI understand the required style and response format. This technique is especially effective for consistent outputs.

"Convert the following user stories into technical tasks.

Example 1:
User Story: As a user I want to reset password
Task: Implement reset password endpoint with email verification

Example 2:
User Story: As admin I want to see statistics
Task: Create dashboard with metrics API and React components

Now convert:
User Story: As a user I want to upload profile picture"

Prompt Templating¶

For recurring tasks, it’s beneficial to create a template system. Modern AI libraries like LangChain or Semantic Kernel offer robust templating mechanisms.

from langchain.prompts import PromptTemplate

code_review_template = PromptTemplate(
    input_variables=["code", "language", "focus_areas"],
    template="""
Perform code review for {language} code.
Focus on: {focus_areas}

Code:
{code}

Response structure:
1. Overall rating (1-10)
2. Found issues
3. Improvement suggestions
4. Security risks
"""
)

Iterative Prompt Optimization¶

Prompt Engineering is an iterative process. We measure performance and gradually optimize based on results.

# Version 1 - general prompt
"Explain this code"

# Version 2 - adding context
"You are a senior developer. Explain this Python code"

# Version 3 - target audience specification
"You are a senior developer. Explain this Python code for a junior developer.
Focus on:
- Code purpose
- Design patterns used
- Possible improvements"

# Version 4 - format optimization
"You are a mentor for junior developers. Analyze this Python code:

{code}

Response structure:
📋 Purpose: What the code does
🔧 Techniques: Used design patterns and concepts  
💡 Improvements: 2-3 specific suggestions
⚠️ Warnings: Potential issues"

Handling Edge Cases¶

Quality prompts account for edge cases and unexpected inputs. We define fallback behavior and validations.

system_prompt = """
You are an AI assistant for code review.

RULES:
- If code contains less than 5 lines, ask for more context
- If language is not recognizable, ask for clarification
- Don't show complete rewritten code, only fragments with explanation
- When detecting security issues, mark them as CRITICAL

FALLBACK:
If you can't analyze the code, explain why and suggest alternative approach.
"""

Testing and Validation¶

Systematic prompt testing is crucial for production deployment. We create test suites with various scenarios.

import pytest
from typing import List

class PromptTester:
    def __init__(self, llm_client, prompt_template):
        self.client = llm_client
        self.template = prompt_template

    def test_scenarios(self, test_cases: List[dict]):
        results = []
        for case in test_cases:
            prompt = self.template.format(**case['input'])
            response = self.client.generate(prompt)

            # Response validation
            score = self.evaluate_response(
                response, 
                case['expected_criteria']
            )
            results.append({
                'input': case['input'],
                'response': response,
                'score': score
            })

        return results

Performance Optimization¶

Efficient prompts save tokens and thus reduce costs. We monitor the ratio of output quality to consumed tokens.

# Inefficient - too detailed
"As a very experienced senior software engineer with 15+ years of practice..."

# Efficient - same effect
"You are a senior developer."

# Token optimization
"Role: Senior dev
Task: Code review
Format: Issue → Solution
Limit: Max 200 words"

Summary¶

Successful Prompt Engineering combines clear structure, appropriate techniques (CoT, few-shot), iterative optimization, and systematic testing. The key is understanding that a prompt is an interface between human intent and AI capabilities. Investment in quality prompts pays back in the form of more accurate results, lower costs, and more reliable AI system behavior. In production environments, it’s essential to implement monitoring, A/B testing, and continuous optimization based on real data.