Domain-Driven Design Concepts Guide for pytest-fixer

Introduction

This guide explains the Domain-Driven Design (DDD) concepts necessary to rebuild pytest-fixer. Each concept is illustrated with concrete examples from our domain.

Table of Contents

  1. Core DDD Concepts
  2. 1. Ubiquitous Language
  3. 2. Bounded Contexts
  4. 3. Aggregates
  5. 4. Entities
  6. 5. Value Objects
  7. 6. Domain Services
  8. 7. Repositories
  9. 8. Domain Events
  10. 9. Application Services
  11. Common DDD Patterns
  12. 1. Factory Pattern
  13. 2. Specification Pattern
  14. 3. Anti-Corruption Layer
  15. DDD Best Practices
  16. Avoiding Common Mistakes
  17. Practical Tips for pytest-fixer

Core DDD Concepts

1. Ubiquitous Language

The shared language between developers and domain experts. For pytest-fixer, this includes:

  • Test Error: A failing pytest test that needs fixing
  • Fix Attempt: A single try at fixing a test error
  • Fix Generation: The process of creating a fix
  • Verification: Checking if a fix works
  • Code Changes: Modifications made to fix an error

Why it matters: Using consistent terminology prevents confusion and misunderstandings. For example, we always say "fix attempt" rather than "try" or "fix iteration".

2. Bounded Contexts

Separate domains with their own models and rules. In pytest-fixer:

  1. Error Analysis Context
  2. Handles test error parsing and analysis
  3. Own concept of what an error means

  4. Focuses on error details and classification

  5. Fix Generation Context

  6. Handles creating and applying fixes
  7. Manages AI interaction

  8. Tracks fix attempts and results

  9. Test Execution Context

  10. Handles running tests
  11. Manages test discovery

  12. Processes test results

  13. Version Control Context

  14. Manages code changes
  15. Handles branching strategy
  16. Controls commit operations

Each context has its own: - Models and rules - Interfaces and services - Data structures and validation

3. Aggregates

Clusters of related objects treated as a single unit. Key aggregates in pytest-fixer:

1. TestError Aggregate

class TestError:  # Aggregate Root
    id: UUID
    test_file: Path
    test_function: str
    error_details: ErrorDetails  # Value Object
    location: CodeLocation      # Value Object
    fix_attempts: List[FixAttempt]  # Child Entity
    status: FixStatus          # Value Object

    def start_fix_attempt(self, temperature: float) -> FixAttempt:
        """Create and track a new fix attempt"""

2. FixSession Aggregate

class FixSession:  # Aggregate Root
    id: UUID
    error: TestError
    current_attempt: Optional[FixAttempt]
    attempts: List[FixAttempt]
    status: FixSessionStatus

Rules for Aggregates: - Only reference other aggregates by ID - Maintain consistency boundaries - Handle transactional requirements

4. Entities

Objects with identity that changes over time. Key entities:

1. FixAttempt

@dataclass
class FixAttempt:
    id: UUID
    error_id: UUID
    attempt_number: int
    temperature: float
    changes: Optional[CodeChanges]
    status: FixStatus

2. TestCase

@dataclass
class TestCase:
    id: UUID
    file_path: Path
    function_name: str
    source_code: str

Entity characteristics: - Have unique identity - Mutable over time - Track state changes - Maintain history

5. Value Objects

Immutable objects without identity. Examples:

@dataclass(frozen=True)
class CodeLocation:
    file_path: Path
    line_number: int
    column: Optional[int] = None
    function_name: Optional[str] = None

@dataclass(frozen=True)
class ErrorDetails:
    error_type: str
    message: str
    stack_trace: Optional[str] = None
    captured_output: Optional[str] = None

@dataclass(frozen=True)
class CodeChanges:
    original: str
    modified: str
    location: CodeLocation
    description: Optional[str] = None

Value Object rules: - Immutable - No identity - Equality based on attributes - Self-validating

6. Domain Services

Services that handle operations not belonging to any entity:

class ErrorAnalysisService:
    """Analyzes test output to create TestError instances"""
    def analyze_error(self, test_output: str, test_file: Path) -> TestError:
        """Extract error information from test output"""

class FixGenerationService:
    """Generates fixes using AI"""
    def generate_fix(self, error: TestError, attempt: FixAttempt) -> CodeChanges:
        """Generate a fix for the error"""

When to use Services: - Operation spans multiple entities - Complex domain logic - External system integration

7. Repositories

Interfaces for persisting and retrieving aggregates:

class TestErrorRepository(Protocol):
    def get_by_id(self, error_id: UUID) -> Optional[TestError]:
        """Retrieve a TestError by ID"""

    def save(self, error: TestError) -> None:
        """Save a TestError"""

    def get_unfixed_errors(self) -> List[TestError]:
        """Get all unfixed errors"""

Repository principles: - One repository per aggregate - Hide storage details - Return fully-loaded aggregates - Handle persistence concerns

8. Domain Events

Notifications of significant changes in the domain:

@dataclass
class FixAttemptStarted:
    error_id: UUID
    attempt_id: UUID
    timestamp: datetime

@dataclass
class FixVerificationCompleted:
    error_id: UUID
    attempt_id: UUID
    success: bool
    verification_output: str

When to use Events: - State changes matter to other contexts - Need to maintain audit trail - Cross-context communication needed

9. Application Services

Orchestrate the use cases of the application:

class TestFixingApplicationService:
    def __init__(
        self,
        error_analysis: ErrorAnalysisService,
        fix_generation: FixGenerationService,
        version_control: VersionControlService,
        error_repository: TestErrorRepository,
        event_publisher: EventPublisher
    ):
        # Initialize dependencies...

    def attempt_fix(self, error_id: UUID, temperature: float = 0.4) -> FixAttempt:
        """Coordinate the process of attempting a fix"""

Application Service responsibilities: - Use case orchestration - Transaction management - Event publishing - Error handling

Common DDD Patterns

1. Factory Pattern

Use factories to create complex aggregates:

class TestErrorFactory:
    def from_test_output(
        self,
        test_output: str,
        test_file: Path,
        test_function: str
    ) -> TestError:
        """Create TestError from test output"""

2. Specification Pattern

Express complex queries or validations:

class FixableErrorSpecification:
    def is_satisfied_by(self, error: TestError) -> bool:
        """Check if error can be fixed"""

3. Anti-Corruption Layer

Protect domain model from external systems:

class AIServiceAdapter:
    """Adapt AI service responses to our domain model"""
    def adapt_response(self, ai_response: dict) -> CodeChanges:
        """Convert AI response to domain model"""

DDD Best Practices

  1. Start with Bounded Contexts
  2. Identify clear boundaries first
  3. Define context interactions

  4. Document context maps

  5. Focus on Behavior

  6. Model behavior, not just data
  7. Use rich domain models

  8. Encapsulate business rules

  9. Use Value Objects

  10. Create immutable value objects
  11. Validate on creation

  12. Make invalid states unrepresentable

  13. Handle Edge Cases

  14. Define error scenarios
  15. Use domain events

  16. Maintain consistency

  17. Test Domain Logic

  18. Unit test aggregates
  19. Test business rules
  20. Mock infrastructure

Avoiding Common Mistakes

  1. Anemic Domain Model
  2. Don't create data-only classes
  3. Include business logic

  4. Use rich behavior

  5. Leaky Abstractions

  6. Keep infrastructure out of domain
  7. Use clean interfaces

  8. Maintain boundaries

  9. Missing Events

  10. Use events for important changes
  11. Track state transitions

  12. Maintain audit trail

  13. Complex Aggregates

  14. Keep aggregates focused
  15. Use proper boundaries
  16. Split if too complex

Practical Tips for pytest-fixer

  1. Start with Core Domain Model (TestError)
  2. Add Behavior Incrementally
  3. Use Events for Tracking
  4. Keep Interfaces Clean
  5. Test Domain Logic Thoroughly

Additional Blueprint: Aligning on Goals

🧙🏾‍♂️: Below is a cohesive, from-scratch rewrite that combines the strengths of previous approaches into a cleaner, domain-driven, and modular architecture. It clarifies domain logic, separates concerns, and provides a strong foundation for future extensions. This blueprint focuses on core functionality: discovering test errors, generating fixes using AI (via a hypothetical AIManager or Coder), applying changes, verifying them, and persisting state. It employs DDD patterns, a clear layering approach, and sets up a workable starting point.

Key Design Principles

  1. Domain-Driven Design (DDD)
  2. Domain Model: Defines TestError, FixAttempt, ErrorDetails, and related entities as the heart of the domain.
  3. Value Objects: CodeLocation, CodeChanges are immutable and model specific domain concepts clearly.
  4. Repositories: Abstract away persistence details behind interfaces.

  5. Domain Services: Provide business logic that doesn't belong inside entities.

  6. Clean Architecture Layers

  7. Domain (Core): Entities, Value Objects, Domain Services, Repository Interfaces.
  8. Application: Orchestrates use cases, coordinates domain objects, and triggers domain services.

  9. Infrastructure: Implementation details like Git-based repository, AI integration, running pytest, file I/O.

  10. Events & Extensibility

  11. Define domain events minimally for the starting point.

  12. Events can be published to other interested parties (e.g., logging, analytics, asynchronous pipelines).

  13. Testing & Configuration

  14. Testing can be added incrementally.
  15. Configuration handled through environment variables or a config file.
  16. Placeholders for integration points (AIManager, TestRunner, VCSManager) to be implemented concretely later.

Project Structure

pytest_fixer/
├── domain/
│   ├── models.py        # Entities, Value Objects
│   ├── events.py        # Domain events
│   ├── repositories.py  # Repository interfaces
│   ├── services.py      # Domain services (e.g., ErrorAnalysis)
│   └── __init__.py
├── application/
│   ├── usecases.py      # Application services (Use cases)
│   ├── dto.py           # Data Transfer Objects if needed
│   └── __init__.py
├── infrastructure/
│   ├── ai_manager.py     # AI integration (fix generation)
│   ├── test_runner.py    # Pytest integration
│   ├── vcs_manager.py    # Git operations
│   ├── repository_impl.py# Git or file-based repository implementation
│   ├── change_applier.py # Applying and reverting code changes
│   └── __init__.py
└── main.py

Detailed Implementation

domain/models.py

from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import List, Optional
from uuid import UUID, uuid4

@dataclass(frozen=True)
class CodeLocation:
    file_path: Path
    line_number: int
    column: Optional[int] = None
    function_name: Optional[str] = None

@dataclass(frozen=True)
class ErrorDetails:
    error_type: str
    message: str
    stack_trace: Optional[str] = None
    captured_output: Optional[str] = None

@dataclass(frozen=True)
class CodeChanges:
    original: str
    modified: str
    description: Optional[str] = None

@dataclass
class FixAttempt:
    id: UUID = field(default_factory=uuid4)
    error_id: UUID = field(default_factory=uuid4)
    attempt_number: int = 1
    temperature: float = 0.4
    changes: Optional[CodeChanges] = None
    status: str = "pending"
    started_at: datetime = field(default_factory=datetime.utcnow)
    completed_at: Optional[datetime] = None

    def mark_success(self, changes: CodeChanges):
        self.changes = changes
        self.status = "success"
        self.completed_at = datetime.utcnow()

    def mark_failure(self):
        self.status = "failed"
        self.completed_at = datetime.utcnow()

@dataclass
class TestError:
    id: UUID = field(default_factory=uuid4)
    test_file: Path = field(default_factory=Path)
    test_function: str = ""
    error_details: ErrorDetails = field(default_factory=ErrorDetails)
    location: CodeLocation = field(default_factory=lambda: CodeLocation(Path("."), 0))
    fix_attempts: List[FixAttempt] = field(default_factory=list)
    status: str = "unfixed"

    def start_fix_attempt(self, temperature: float) -> FixAttempt:
        attempt = FixAttempt(
            error_id=self.id,
            attempt_number=len(self.fix_attempts) + 1,
            temperature=temperature
        )
        self.fix_attempts.append(attempt)
        return attempt

    def mark_fixed(self, attempt: FixAttempt):
        attempt.status = "success"
        self.status = "fixed"

    def mark_attempt_failed(self, attempt: FixAttempt):
        attempt.mark_failure()

    def can_attempt_fix(self) -> bool:
        return self.status == "unfixed"

domain/events.py

from dataclasses import dataclass
from datetime import datetime
from uuid import UUID

@dataclass
class FixAttemptStarted:
    error_id: UUID
    attempt_id: UUID
    timestamp: datetime

@dataclass
class FixAttemptCompleted:
    error_id: UUID
    attempt_id: UUID
    success: bool
    timestamp: datetime

domain/repositories.py

from abc import ABC, abstractmethod
from typing import Optional, List
from uuid import UUID

from .models import TestError

class TestErrorRepository(ABC):
    @abstractmethod
    def get_by_id(self, error_id: UUID) -> Optional[TestError]:
        pass

    @abstractmethod
    def save(self, error: TestError) -> None:
        pass

    @abstractmethod
    def get_unfixed_errors(self) -> List[TestError]:
        pass

domain/services.py

import re
from pathlib import Path
from typing import Optional, List
from .models import ErrorDetails, TestError, CodeLocation

class ErrorAnalysisService:
    def analyze_errors(self, test_output: str) -> Optional[List[TestError]]:
        # Basic regex-based approach to find failing tests:
        # Placeholder: Real logic might integrate directly with pytest APIs.
        pattern = r"(.*?::(.*?) FAILED (.*)\n([\s\S]*?)(?=\n\n|$))"
        matches = re.finditer(pattern, test_output)
        errors = []
        for m in matches:
            file_path, test_func, err_type, details = m.groups()
            location = CodeLocation(Path(file_path), 0)
            err_details = ErrorDetails(
                error_type=err_type.strip(),
                message=details.strip(),
                stack_trace=details
            )
            errors.append(TestError(
                test_file=Path(file_path),
                test_function=test_func,
                error_details=err_details,
                location=location
            ))
        return errors if errors else None

application/usecases.py

from typing import Optional
from uuid import UUID

from ..domain.models import TestError
from ..domain.repositories import TestErrorRepository
from ..infrastructure.ai_manager import AIManager
from ..infrastructure.test_runner import TestRunner
from ..infrastructure.vcs_manager import VCSManager
from ..infrastructure.change_applier import ChangeApplier

class TestFixingService:
    def __init__(
        self,
        error_repo: TestErrorRepository,
        ai_manager: AIManager,
        test_runner: TestRunner,
        vcs_manager: VCSManager,
        change_applier: ChangeApplier,
        initial_temp: float = 0.4,
        temp_increment: float = 0.1,
        max_retries: int = 3
    ):
        self.error_repo = error_repo
        self.ai_manager = ai_manager
        self.test_runner = test_runner
        self.vcs = vcs_manager
        self.change_applier = change_applier
        self.initial_temp = initial_temp
        self.temp_increment = temp_increment
        self.max_retries = max_retries

    def discover_and_record_errors(self) -> None:
        stdout, stderr = self.test_runner.run_all_tests()
        analysis_service = self.test_runner.get_analysis_service()
        errors = analysis_service.analyze_errors(stdout + stderr)
        if not errors:
            return
        for e in errors:
            self.error_repo.save(e)

    def attempt_fix(self, error_id: UUID) -> bool:
        error = self.error_repo.get_by_id(error_id)
        if not error or not error.can_attempt_fix():
            return False

        temperature = self.initial_temp
        for _ in range(self.max_retries):
            attempt = error.start_fix_attempt(temperature)
            # Generate fix
            changes = self.ai_manager.generate_fix(error, temperature)
            if not changes:
                # No fix generated, increase temp and continue
                error.mark_attempt_failed(attempt)
                temperature += self.temp_increment
                continue

            # Apply changes
            self.change_applier.apply(changes, error.location)

            # Verify fix
            passed = self.test_runner.run_test_and_check(
                error.test_file, error.test_function
            )

            if passed:
                # Commit changes to VCS
                self.vcs.commit_changes(f"Fix {error.test_file}:{error.test_function}")
                attempt.mark_success(changes)
                error.mark_fixed(attempt)
                self.error_repo.save(error)
                return True
            else:
                # Revert changes and mark attempt failed
                self.change_applier.revert()
                error.mark_attempt_failed(attempt)
                self.error_repo.save(error)
                temperature += self.temp_increment

        # No success after max retries
        self.error_repo.save(error)
        return False

infrastructure/ai_manager.py

from typing import Optional
from ..domain.models import TestError, CodeChanges

class AIManager:
    def __init__(self, model_name: str = "gpt-4"):
        self.model_name = model_name

    def generate_fix(self, error: TestError, temperature: float) -> Optional[CodeChanges]:
        # Placeholder logic - integrate with OpenAI or Aider coder
        # Construct a prompt from error details
        prompt = f"Fix this test error:\n{error.error_details.message}"
        # Here you'd call the AI API or Aider coder to get a changed version of the file
        # For demonstration, let's pretend we got a changed file content:
        modified = error.location.file_path.read_text().replace("bug", "fix")
        original = error.location.file_path.read_text()
        return CodeChanges(original=original, modified=modified, description="AI suggested fix")

infrastructure/test_runner.py

from typing import Tuple
from .ai_manager import AIManager
from ..domain.services import ErrorAnalysisService
from pathlib import Path
import subprocess

class TestRunner:
    def __init__(self, project_dir: Path):
        self.project_dir = project_dir
        self.analysis_service = ErrorAnalysisService()

    def run_all_tests(self) -> Tuple[str, str]:
        cmd = ["pytest", "-v"]
        result = subprocess.run(cmd, cwd=self.project_dir, capture_output=True, text=True)
        return result.stdout, result.stderr

    def run_test_and_check(self, test_file: Path, test_function: str) -> bool:
        cmd = ["pytest", "-q", f"{test_file}::{test_function}"]
        result = subprocess.run(cmd, cwd=self.project_dir, capture_output=True, text=True)
        # If it returns 0 and '1 passed' is in stdout, we consider it passed
        return (result.returncode == 0 and "1 passed" in result.stdout.lower())

    def get_analysis_service(self) -> ErrorAnalysisService:
        return self.analysis_service

infrastructure/vcs_manager.py

import subprocess
from pathlib import Path

class VCSManager:
    def __init__(self, project_dir: Path):
        self.project_dir = project_dir

    def commit_changes(self, message: str):
        subprocess.run(["git", "add", "."], cwd=self.project_dir, check=True)
        subprocess.run(["git", "commit", "-m", message], cwd=self.project_dir, check=True)

infrastructure/change_applier.py

from ..domain.models import CodeChanges, CodeLocation

class ChangeApplier:
    def __init__(self):
        self.backup_content = None
        self.target_file = None

    def apply(self, changes: CodeChanges, location: CodeLocation):
        self.target_file = location.file_path
        self.backup_content = self.target_file.read_text()
        self.target_file.write_text(changes.modified)

    def revert(self):
        if self.target_file and self.backup_content is not None:
            self.target_file.write_text(self.backup_content)

infrastructure/repository_impl.py

from uuid import UUID
from typing import Optional, List
from pathlib import Path
import json

from ..domain.models import TestError
from ..domain.repositories import TestErrorRepository

class FileBasedTestErrorRepository(TestErrorRepository):
    """Simple file-based repository using JSON to store errors."""
    def __init__(self, storage_path: Path):
        self.storage_path = storage_path
        self.storage_path.mkdir(parents=True, exist_ok=True)

    def _get_error_file(self, error_id: UUID) -> Path:
        return self.storage_path / f"{error_id}.json"

    def get_by_id(self, error_id: UUID) -> Optional[TestError]:
        f = self._get_error_file(error_id)
        if not f.exists():
            return None
        data = json.loads(f.read_text())
        return self._deserialize_testerror(data)

    def save(self, error: TestError) -> None:
        f = self._get_error_file(error.id)
        f.write_text(json.dumps(self._serialize_testerror(error), indent=2))

    def get_unfixed_errors(self) -> List[TestError]:
        errors = []
        for f in self.storage_path.glob("*.json"):
            data = json.loads(f.read_text())
            err = self._deserialize_testerror(data)
            if err.status == "unfixed":
                errors.append(err)
        return errors

    def _serialize_testerror(self, error: TestError) -> dict:
        return {
            "id": str(error.id),
            "test_file": str(error.test_file),
            "test_function": error.test_function,
            "status": error.status,
            "error_details": {
                "error_type": error.error_details.error_type,
                "message": error.error_details.message,
                "stack_trace": error.error_details.stack_trace,
                "captured_output": error.error_details.captured_output
            },
            "location": {
                "file_path": str(error.location.file_path),
                "line_number": error.location.line_number,
                "column": error.location.column
            },
            "fix_attempts": [
                {
                    "id": str(a.id),
                    "error_id": str(a.error_id),
                    "attempt_number": a.attempt_number,
                    "temperature": a.temperature,
                    "status": a.status,
                    "started_at": a.started_at.isoformat(),
                    "completed_at": a.completed_at.isoformat() if a.completed_at else None,
                    "changes": {
                        "original": a.changes.original if a.changes else None,
                        "modified": a.changes.modified if a.changes else None,
                        "description": a.changes.description if a.changes else None
                    } if a.changes else None
                } for a in error.fix_attempts
            ]
        }

    def _deserialize_testerror(self, data: dict) -> TestError:
        from datetime import datetime
        from uuid import UUID
        from ..domain.models import ErrorDetails, CodeLocation, CodeChanges, FixAttempt, TestError
        fix_attempts = []
        for a in data.get("fix_attempts", []):
            changes = a["changes"]
            fix_attempts.append(FixAttempt(
                id=UUID(a["id"]),
                error_id=UUID(a["error_id"]),
                attempt_number=a["attempt_number"],
                temperature=a["temperature"],
                status=a["status"],
                started_at=datetime.fromisoformat(a["started_at"]),
                completed_at=datetime.fromisoformat(a["completed_at"]) if a["completed_at"] else None,
                changes=CodeChanges(**changes) if changes and changes["original"] else None
            ))

        return TestError(
            id=UUID(data["id"]),
            test_file=Path(data["test_file"]),
            test_function=data["test_function"],
            status=data["status"],
            error_details=ErrorDetails(**data["error_details"]),
            location=CodeLocation(
                Path(data["location"]["file_path"]),
                data["location"]["line_number"],
                data["location"]["column"]
            ),
            fix_attempts=fix_attempts
        )

main.py (Example Entry Point)

import sys
from pathlib import Path
from uuid import UUID

from pytest_fixer.domain.repositories import TestErrorRepository
from pytest_fixer.infrastructure.repository_impl import FileBasedTestErrorRepository
from pytest_fixer.infrastructure.ai_manager import AIManager
from pytest_fixer.infrastructure.test_runner import TestRunner
from pytest_fixer.infrastructure.vcs_manager import VCSManager
from pytest_fixer.infrastructure.change_applier import ChangeApplier
from pytest_fixer.application.usecases import TestFixingService

def main():
    project_dir = Path(".")
    storage_path = project_dir / ".pytest_fixer_storage"
    error_repo: TestErrorRepository = FileBasedTestErrorRepository(storage_path)
    ai_manager = AIManager(model_name="gpt-4")
    test_runner = TestRunner(project_dir)
    vcs_manager = VCSManager(project_dir)
    change_applier = ChangeApplier()

    service = TestFixingService(
        error_repo=error_repo,
        ai_manager=ai_manager,
        test_runner=test_runner,
        vcs_manager=vcs_manager,
        change_applier=change_applier,
        initial_temp=0.4,
        temp_increment=0.1,
        max_retries=3
    )

    # Discover new errors
    service.discover_and_record_errors()

    # Attempt fix on all unfixed errors
    unfixed = error_repo.get_unfixed_errors()
    for err in unfixed:
        print(f"Attempting to fix error {err.id} in {err.test_file}:{err.test_function}")
        success = service.attempt_fix(err.id)
        if success:
            print(f"Error {err.id} fixed!")
        else:
            print(f"Failed to fix error {err.id}")

if __name__ == "__main__":
    main()

Next Steps

  • Add Tests: Unit tests for domain models, application services, and repositories.
  • Robust Error Analysis: Improve parsing logic to handle real pytest output.
  • Real AI Integration: Implement AIManager to communicate with OpenAI or a local LLM.
  • Enhanced Verification: Capture test output logs, enable incremental verification, and implement more robust revert strategies.
  • Event Handling: Integrate event dispatchers or log all domain events as needed.
  • Configuration & Logging: Integrate a configuration file or environment variables and a structured logging solution.

This setup is cleaner, better modularized, and follows best practices by combining a domain-driven structure with a clear separation of concerns. It enhances maintainability and extensibility, providing a solid foundation that can be refined and expanded as the project’s complexity grows.

Visual Enhancements

To further enhance the readability and visual appeal of your Markdown document, consider the following tips:

  • Consistent Heading Levels: Ensure that heading levels (#, ##, ###, etc.) are used consistently to represent the document structure.
  • Code Blocks: Use triple backticks (```) for code blocks with proper syntax highlighting by specifying the language (e.g., ```python).
  • Lists and Indentation: Use bullet points or numbered lists to organize information clearly.
  • Bold and Italics: Highlight key terms and important points using bold or italics.
  • Tables: For comparing options or presenting structured data, use Markdown tables.
  • Spacing: Add blank lines between sections and elements to prevent clutter and improve readability.