Optimization Areas
- Model preset selection for cost and performance balance
- Execution mode configuration for workflow efficiency
- Context management for large codebases
- Per-project and workspace-level customization
Tuning Settings for Your Workflow
Model Preset Selection
Choose the appropriate model preset based on task complexity and budget:| Preset | Cost | Cost Efficiency | Best For |
|---|---|---|---|
| Efficiency | ~0.3x Balance (70% reduction) | 3.2x more efficient than Sonnet | Simple tasks, quick fixes, rapid prototyping |
| Balance (Default) | 1x baseline | 1x baseline | General development tasks, everyday coding |
| Performance | 1-2x Balance | 0.5x efficiency (2x cost, 1M context) | Complex architectural decisions, intricate refactoring |
- Claude-4.5-sonnet: Balanced general-purpose model (1x baseline)
- Claude-4.5-sonnet-1m: Extended context for complex tasks (0.5x efficiency, 2x cost when input exceeds 200k tokens)
- Claude-4.5-haiku: Fastest, most economical (3.2x more efficient than Sonnet)
- GPT-5 / GPT-5-codex: Reasoning and creativity focus (Beta, 1.3x more efficient than Sonnet)
- Minimax-m2: Fast and cost-effective (Beta, free until November 10th)
Execution Mode Configuration
- Skip Permissions (Speed)
- Auto-Run (Balanced)
- Manual Accept (Thoroughness)
- Plan (Review)
- Full autonomy without approval prompts
- No interruptions for permissions
- Highest risk - use only in automated environments
Think Hard Mode
Enable for complex reasoning tasks:- Thoroughness: Maximum reasoning depth for sophisticated problems
- Speed: Slower due to extended thinking budget
- Best For: Architectural decisions, complex debugging, intricate analysis
Speed vs Thoroughness Tradeoffs
Balance execution speed against analysis depth based on task requirements.- Speed-Critical
- Thoroughness-Critical
- Hybrid Approach
Configuration:
- Model: Efficiency preset (Claude-4.5-haiku)
- Permission Mode: Auto-Run Mode
- Execution Mode: Agent Mode for direct execution
- Use Cases: Quick fixes, routine operations, simple tasks
- Fastest response times
- Minimal interruptions
- 70% cost reduction vs Balance preset
Workspace-Level Configuration
Verdent supports per-project and per-workspace configuration for team-wide consistency.Project-Specific Configuration
Project Rules (AGENTS.md):- Location: Project root directory or workspace folder
- Scope: Applied only to the current project
- Version Control: Commit to git for team-wide standards
- Content: Coding standards, testing requirements, architectural patterns
VS Code Workspace Settings
Configure extension settings at workspace level: Location:.vscode/settings.json in workspace root
Example:
Configuration Priority
When configurations conflict, Verdent applies this priority order:- Project Rules (AGENTS.md) - Highest priority (project-specific)
- Workspace Settings - VS Code workspace-level settings
- User Rules (VERDENT.md) - Global user preferences
- Default Settings - Verdent’s built-in defaults
Project-Specific Customization
Context Management for Large Projects
- Subagents
- File Selection
- Task Chunking
- Plan Mode
- Delegate complex operations to subagents with separate context windows
- Only subagent results consume main context, not entire process
- Prevents main context from filling with implementation details
Performance Optimization
Enable Checkpoints Selectively: Theverdent.enableCheckpoints setting uses git for version control:
- May impact performance on very large repositories
- Enable only when checkpoint functionality is needed
- Disable for maximum performance on large codebases
- Use Efficiency preset (Haiku) for simple, isolated tasks
- Reserve Performance preset (Sonnet-1M) for context-heavy operations
- Balance preset for general work
- Auto-Run Mode reduces context consumed by permission prompts
- Skip Permissions Mode maximizes efficiency for automated environments
Multi-workspace scenarios automatically apply appropriate project rules when switching workspaces. No manual configuration switching required.
See Also
Configuration Settings
Complete configuration options and model selection
Resource Monitoring
Monitor performance and optimize token usage