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Deep Research & Web Search

How PlanToCode performs web searches, processes results, and integrates findings into development workflows.

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8 min read
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The Deep Research feature enables PlanToCode to perform intelligent AI-powered research, gather relevant information, and integrate findings directly into development workflows. This system uses large language models to generate targeted research queries based on your project context, execute parallel research tasks, and synthesize actionable insights to enhance code generation and problem-solving capabilities.

Deep Research Pipeline

The two-stage workflow: prompt generation and parallel research execution.

Deep research pipeline diagram showing prompt generation and execution stages
Click to expand
Deep research workflow showing prompt generation and parallel execution stages

Architecture Overview

The deep research system operates as a two-stage workflow: (1) WebSearchPromptsGeneration - AI analyzes your task and project context to generate targeted research queries, and (2) WebSearchExecution - the LLM executes research prompts in parallel and synthesizes findings. Each stage is designed for reliability, cost efficiency, and contextual relevance.

Research Workflow Stages

Prompt Generation

Research prompts are automatically generated by AI based on your task description, project context, and included files. The system analyzes your codebase structure via directory tree and file contents to formulate targeted research queries. Up to 12 focused research prompts are generated per task.

Research Topics

  • • API documentation and library-specific research
  • • Error resolution and debugging approaches
  • • Best practices and recommended patterns
  • • Version compatibility and migration paths
  • • Security considerations and vulnerability awareness

Research Execution

Research prompts are executed in parallel by AI language models. Each prompt is processed independently, allowing the system to gather information on multiple aspects of your task simultaneously. Results are synthesized into structured findings with titles and actionable insights.

Research Focus Areas

  • • API documentation and technical specifications
  • • Code examples and implementation patterns
  • • Error resolution and troubleshooting approaches
  • • Best practices and implementation patterns
  • • Version compatibility and migration guidance

Result Processing & Synthesis

Research findings are structured into JSON format with titles and detailed findings. The system aggregates results from parallel research tasks, tracks success and failure counts, and provides a summary of the research outcomes. Results are stored in job metadata for easy access.

Processing Steps

  • • Key findings extracted and formatted for integration
  • • Results organized by research topic and relevance
  • • Findings consolidated across multiple research prompts
  • • Research execution tracked with timing metrics
  • • Actionable insights and recommendations highlighted

API Integration Details

AI Research Configuration

The system uses your configured LLM provider for web research. The LLM generates targeted research queries based on your task context and synthesizes findings from its training data and web search capabilities. Model selection and configuration are managed through the application settings.

// Start the web search workflow (Tauri command)
await invoke("start_web_search_workflow", {
  sessionId,
  taskDescription,
  projectDirectory,
  excludedPaths: ["node_modules", "dist"],
  timeoutMs: 300000,
});

Content Processing Pipeline

Research findings pass through a standardized processing pipeline that extracts meaningful information while preserving formatting and context. The pipeline handles various content types and synthesizes findings into actionable insights for development workflows.

// WebSearchExecution response (stored in job.response)
{
  "searchResults": [
    { "title": "Research Task 1", "findings": "Summary text..." }
  ],
  "searchResultsCount": 1,
  "summary": "Found 1 research findings"
}

Development Workflow Integration

Context-Aware Research

Research requests are automatically enhanced with context from your current session. The system includes your project's directory tree and selected file contents in the prompt generation phase, enabling the AI to formulate research queries that are specific to your codebase.

Result Integration

Research findings can be used to inform implementation plans. When research tasks complete, findings are formatted as research_finding tags that can be incorporated into subsequent planning tasks, ensuring your implementation is guided by current best practices and accurate documentation.

Result Storage

Research results are stored in job metadata and can be accessed through the job details panel. Results persist for the session duration and can be referenced when creating implementation plans or making coding decisions.

Configuration and Customization

Research Settings

Research behavior is configured through model selection and task settings. Choose your preferred AI model for research tasks, configure timeouts, and select which files to include for context.

Configurable Options

  • • Project directory and file selection for context
  • • Model selection determines research quality and cost
  • • Maximum 12 research prompts generated per task
  • • Start research manually via the workflow command
  • • Include relevant project files for better context

Project-Specific Settings

Research configuration is session-aware. The system uses the current session's project directory and included files to provide context. Excluded paths (like node_modules, dist) are automatically filtered from the directory tree shown to the AI.

Cost Considerations

Usage and Costs

Deep research uses your configured LLM provider. Each research task generates multiple parallel LLM calls, so costs scale with the number of research prompts generated. The system tracks token usage and costs per job for transparency.

Cost Management Tips

  • • Token usage tracked per research job with detailed cost breakdown
  • • Costs managed through your provider account (self-hosted) or PlanToCode billing (hosted)
  • • Monitor job metadata for token counts and estimated costs
  • • Research results are stored per job so you can reuse them without rerunning

Cost Optimization

Research costs are managed through intelligent prompt generation - the system limits research prompts to a maximum of 12 per task. Parallel execution minimizes wall-clock time. Each job tracks token usage and estimated costs in its metadata for full transparency.

Best Practices and Examples

Effective Search Strategies

To maximize the value of web search integration, follow these proven strategies for formulating queries, interpreting results, and integrating findings into your development workflow.

Query Formulation

  • • Include specific version numbers when relevant
  • • Combine library names with specific error messages
  • • Use "best practices" or "recommended approach" for pattern searches
  • • Include platform or environment constraints

Result Evaluation

  • • Prioritize official documentation over third-party sources
  • • Check publication dates for time-sensitive information
  • • Verify code examples in your development environment
  • • Cross-reference solutions across multiple sources

Integration Examples

Common integration patterns demonstrate how web search results enhance different development scenarios, from debugging specific errors to implementing new features with unfamiliar APIs.

// Example: API integration research
Search query: "Next.js 14 app router middleware authentication"
Results integrated as:
- Middleware setup code with current best practices
- Authentication flow documentation links
- Common pitfalls and troubleshooting tips
- Compatible library recommendations

Troubleshooting and Support

Common Issues

Most research issues stem from LLM API connectivity, insufficient credits, or prompts that are too broad. The system provides clear error messages and job status tracking for troubleshooting.

API Errors

Check provider status and rate limit or credit balance

No Research Prompts Generated

Provide more specific task descriptions or include relevant files for context

Model Availability

Some models may have regional restrictions from the provider

Performance Optimization

For optimal performance, provide clear and specific task descriptions. Include relevant project files to give the AI better context. The system executes research prompts in parallel to minimize total execution time.

Ready to use Deep Research?

The Deep Research and Web Search features are available in the PlanToCode desktop application. Download the build for your platform to start integrating web research into your development workflow.