Practical Guide to Programming with LLMs

Fundamental Principles of LLM Programming

Programming with LLMs (Large Language Models) is fundamentally transforming traditional development methods. As of 2025, LLMs have evolved from simple code completion tools into comprehensive development partners.

Basic Concepts

1. Human-AI Collaboration: LLMs are tools for augmentation, not replacement
2. Iterative Improvement: Don't aim for perfection in one go; improve incrementally
3. Context-Driven: Providing rich information yields better results
4. Validation Habit: Always verify and test LLM outputs with human review

Development Workflow Design

1. Project Initiation Workflow

graph TD
    A[Requirements Analysis] --> B[LLM-Generated Design Proposal]
    B --> C[Human Design Review]
    C --> D[Detailed Specification]
    D --> E[Architecture Design]
    E --> F[Implementation Planning]

Practical Example

# 1. Consult LLM for requirements analysis
prompt = """
Analyze the following requirements and propose a technical specification:

Requirements:
- Web-based task management system
- User authentication
- Task creation, editing, deletion
- Team sharing functionality
- Real-time updates

Constraints:
- Development period: 3 months
- Team: 2 frontend, 2 backend developers
- Budget: ≤50,000 yen/month for system operation

Please propose:
1. Technology stack
2. Architecture overview
3. Implementation priority for major features
4. Security considerations
"""

2. Implementation Phase Workflow

# Incremental implementation approach
class DevelopmentPhase:
    def __init__(self):
        self.phases = [
            "PoC (Proof of Concept)",
            "MVP (Minimum Viable Product)", 
            "Feature Expansion",
            "Performance Optimization",
            "Production Readiness"
        ]

    def execute_phase(self, phase, llm_assistant):
        """Leverage LLM in each phase"""
        requirements = self.get_phase_requirements(phase)
        implementation = llm_assistant.generate_code(requirements)
        tested_code = self.test_and_validate(implementation)
        return tested_code

Effective Prompt Design Patterns

1. Hierarchical Prompts

# Bad example: too vague
"Create a login feature in Python"

# Good example: structured prompt
prompt = """
【System Overview】
- Web API using FastAPI
- JWT authentication implementation
- User information management with PostgreSQL

【Requirements Specification】
1. User registration endpoint (POST /register)
2. Login endpoint (POST /login)
3. User info retrieval (GET /me)
4. Password hashing (using bcrypt)

【Technical Constraints】
- Python 3.11
- FastAPI 0.104+
- SQLAlchemy 2.0
- Pydantic v2

【Security Requirements】
- Password minimum 8 characters, alphanumeric + symbols
- JWT expiration 1 hour
- Refresh token functionality

【Expected Output】
1. Code implementation
2. Test cases
3. API documentation (OpenAPI format)
4. Security checklist
"""

2. Context Injection Pattern

# Prompt with project context
context_prompt = f"""
【Project Context】
Project name: {project_name}
Architecture: {architecture_type}
Tech stack: {tech_stack}
Coding standards: {coding_standards}
Testing strategy: {testing_strategy}

【Existing Code Structure】
{code_structure}

【Current Task】
{current_task}

Please implement considering the above context.
"""

3. Error Correction Pattern

debug_prompt = """
【Error Situation】
Error message: {error_message}
Location: {error_location}
Stack trace: {stack_trace}

【Environment Information】
OS: {os_info}
Python version: {python_version}
Dependencies: {dependencies}

【Related Code】
{relevant_code}

【Attempted Solutions】
{attempted_solutions}

【Desired Solution】
1. Root cause of the error
2. Fix method (including code)
3. Prevention measures
4. Related potential issues
"""

Code Quality Improvement Techniques

1. Review-Driven Development

# Example of code review using LLM
review_prompt = """
Please review the following code:

【Review Perspectives】
1. Readability & maintainability
2. Performance
3. Security
4. Error handling
5. Testability
6. Design principle adherence

【Code】
{code_to_review}

【Project-Specific Constraints】
- Processing time must be within 1 second
- Memory usage under 100MB
- Support up to 1000 concurrent connections

【Output Format】
- Issue identification with severity (High/Medium/Low)
- Specific improvement suggestions
- Refactored code
"""

2. Test-Driven Development (TDD) Support

# Example prompt for test-first development
tdd_prompt = """
Please create test cases first based on the following specification:

【Function Specification】
{function_specification}

【Testing Strategy】
- Unit tests (using pytest)
- Boundary value testing
- Exception testing
- Mocks and stubs

【Expected Test Cases】
1. Happy path tests
2. Exception tests
3. Boundary value tests
4. Performance tests

After creating tests, please also provide implementation code.
"""

3. Refactoring Support

refactoring_prompt = """
Please refactor the following legacy code into modern Python:

【Target Code】
{legacy_code}

【Refactoring Goals】
1. Add type hints
2. Single Responsibility Principle for functions
3. Improve error handling
4. Add docstrings
5. Testable structure

【Constraints】
- Do not change existing API
- Maintain or improve performance
- Leverage Python 3.11 features

【Output】
1. Refactored code
2. Explanation of changes
3. Migration guide
"""

Performance Optimization

1. Profiling Support

# Performance analysis prompt
performance_prompt = """
Please analyze and improve the performance of the following code:

【Current Problem】
- Processing time: {current_time}s (target: {target_time}s)
- Memory usage: {current_memory}MB (target: {target_memory}MB)
- Data size: {data_size} records

【Target Code】
{performance_critical_code}

【Analysis Items】
1. Bottleneck identification
2. Algorithm complexity analysis
3. Memory efficiency evaluation
4. I/O processing optimization

【Improvement Proposal】
1. Optimized code
2. Benchmark comparison
3. Alternative algorithm proposals
"""

2. Parallel Processing Implementation

# Guidance for parallel processing implementation
concurrency_prompt = """
Please parallelize the following process for speedup:

【Processing Content】
{sequential_processing_code}

【Requirements】
- Data volume: {data_volume}
- Available CPUs: {cpu_cores} cores
- Memory limit: {memory_limit}GB

【Parallelization Strategy】
1. Multiprocessing
2. Multithreading
3. Asynchronous processing
4. Batch processing

【Considerations】
- Data dependencies
- Shared resource access
- Error handling
- Progress monitoring
"""

Debugging and Troubleshooting

1. Systematic Debugging Approach

class LLMDebuggingAssistant:
    def analyze_error(self, error_info):
        prompt = f"""
        【Error Analysis Request】

        1. Error classification
        2. Most likely causes
        3. Investigation procedure
        4. Proposed fixes

        【Error Information】
        {error_info}

        【Environment Information】
        {self.get_environment_info()}
        """
        return self.llm.generate(prompt)

    def suggest_debugging_strategy(self, problem_description):
        return """
        1. Add logging output
        2. Create unit tests
        3. Visualize data flow
        4. Measure performance
        5. Check external dependencies
        """

2. Log Analysis Support

log_analysis_prompt = """
Please analyze the following logs to identify issues:

【Log Data】
{log_data}

【System Information】
- Application: {app_name}
- Environment: {environment}
- Timezone: {timezone}

【Analysis Items】
1. Identify error patterns
2. Problem occurrence trends over time
3. Correlation of related events
4. Anomalies in performance metrics

【Output】
1. Problem summary
2. Root cause estimation
3. Countermeasure proposals
4. Monitoring improvement suggestions
"""

Security Best Practices

1. Security Review

security_review_prompt = """
Please evaluate the security of the following code:

【Target Code】
{code_for_security_review}

【Checklist】
1. Input validation
2. SQL injection prevention
3. XSS prevention
4. Authentication & authorization
5. Data encryption
6. Log output security

【Compliance Requirements】
- GDPR compliance
- SOC2 Type II
- PCI DSS (for payment-related)

【Output】
1. Vulnerability identification
2. Risk assessment (Critical/High/Medium/Low)
3. Fix methods
4. Security test cases
"""

2. Secure API Design

# Secure API design support
api_security_prompt = """
Please enhance the security of the following API specification:

【Current API Specification】
{current_api_spec}

【Security Requirements】
1. Authentication mechanism (JWT/OAuth2)
2. Rate limiting
3. Data validation
4. Audit logging
5. HTTPS enforcement

【Output】
1. Security-enhanced API specification
2. Security implementation code
3. Security testing procedure
4. Operational security checklist
"""

LLM Usage in Team Development

1. Standardized Code Reviews

# Team review template
team_review_template = """
【Review Items】(Team-wide)
□ Coding standards compliance
□ Test coverage ≥80%
□ Documentation updated
□ Security check
□ Performance impact assessment

【Project-Specific Items】
□ {project_specific_checks}

【LLM Review Results】
{llm_review_results}

【Human Reviewer Decision】
□ Approve
□ Request changes
□ Discussion needed
"""

2. Knowledge Sharing Promotion

knowledge_sharing_prompt = """
Please create technical sharing materials for the team about the following implementation:

【Implementation Details】
{implementation_details}

【Target Audience】
- Experience level: {team_experience_level}
- Technical background: {technical_background}

【Document Structure】
1. Overview & background
2. Technology selection rationale
3. Implementation highlights
4. Caveats & pitfalls
5. References & links

【Output Format】
- Markdown format
- Include code examples
- Include diagrams (PlantUML, etc.)
"""

Continuous Learning and Improvement

1. Understanding Technology Trends

trend_analysis_prompt = """
Please research the latest trends in the following technology domain
and evaluate applicability to the project:

【Technology Domain】
{technology_domain}

【Current Project State】
{project_current_state}

【Evaluation Criteria】
1. Technical benefits
2. Adoption cost & risks
3. Learning cost
4. Competitive advantage
5. Long-term maintainability

【Output】
1. Trend analysis report
2. Applicability assessment
3. Adoption plan proposal
4. Risk mitigation measures
"""

2. Skill Development Plan

skill_development_prompt = """
Please analyze the current skill set and propose a learning plan:

【Current Skills】
{current_skills}

【Project Required Skills】
{required_skills}

【Career Goals】
{career_goals}

【Constraints】
- Study time: {study_hours} hours/week
- Duration: {timeframe}
- Budget: {budget}

【Learning Plan】
1. Prioritized skill list
2. Learning resources (books, courses, projects)
3. Milestone setting
4. Progress measurement method
"""

Summary

Programming with LLMs has the potential to significantly improve developer productivity and creativity. However, effective utilization requires:

1. Clear Communication: Instructions to LLMs should be specific and structured
2. Incremental Approach: Divide complex problems into smaller parts
3. Validation Habit: Always verify and test LLM outputs with human review
4. Continuous Learning: Actively try new methods and features
5. Team-wide Adoption: Share best practices across the entire team, not just individuals

LLMs are not replacing developers; they are tools that enable developers to focus on more advanced and creative work. With proper utilization, it becomes possible to develop better software more efficiently.