Which AI model does text improvement use?

Default: Claude Sonnet 3.5 via OpenRouter. You can configure different models per project in settings.

Does it work in code editors?

Yes. Works in task description fields and Monaco code editors. Select text → popover appears → choose action.

What if I edit text while improvement is running?

Conflict detection prevents overwriting your edits. If text changed during processing, the improvement is skipped with a warning.

Specification Capture Mode • Two AI Prompt Types

Specification Capture Mode

Q: Can I customize the improvement prompts?

Yes. Define per-project system prompts with placeholders for file contents, directory tree, custom instructions, and more.

Specification Capture Mode provides TWO DISTINCT AI prompt types designed to help corporate teams capture and refine requirements that development teams can confidently implement.

Text Enhancement improves clarity and grammar. Task Refinement expands completeness and implementation readiness. Both are essential tools for corporate teams managing complex development workflows and detailed requirements gathering.

$5 free credits • Pay-as-you-go • Works on macOS and Windows

Two Distinct AI Prompt Types

Text Enhancement

Improves grammar, sentence structure, clarity, and conciseness while maintaining the user's original intent, tone, and technical detail level. Perfect for polishing rough drafts and voice transcriptions.

Preserves original meaning and intent
Fixes grammar and punctuation
Improves sentence flow and readability
Maintains technical accuracy

Task Refinement

Expands task descriptions by identifying implied requirements, filling in overlooked gaps, clarifying expected behavior and edge cases, and adding technical considerations to make tasks more complete and implementation-ready.

Identifies implied requirements
Clarifies edge cases and constraints
Adds technical implementation details
Makes tasks actionable for developers

Both prompt types leverage large language models to enhance content while preserving the original intent and core meaning of user input. This ensures your specifications remain true to your vision while becoming clearer and more complete.

Why corporate teams use Specification Capture Mode

Vague requirements lead to wrong implementations

AI generates code based on unclear task descriptions, missing critical constraints and edge cases.

Rewriting task descriptions wastes time

Manual refinement of ambiguous descriptions slows down planning and creates misalignment.

Mental models get lost in translation

What you envision doesn't match what gets written down, leading to implementation drift.

How it works

Select text in task description

Highlight any text in your task description or code editor. A popover appears instantly.

Choose "Improve" or "Refine Task"

Improve: Quick style/clarity fixes. Refine Task: Deep contextual enhancement with project files.

AI processes with full context

Claude Sonnet 3.5 analyzes with project files, directory structure, and customizable prompts.

Refined text applied inline

Improved text replaces selection automatically. Preserves formatting and cursor position.

Key capabilities

Context-aware refinement

AI understands your project structure, included files, and custom instructions when refining text.

Customizable system prompts

Define per-project prompts with placeholders for file contents, directory tree, and custom instructions.

Mental model capture

Iterative refinement helps AI understand your intent. Select → refine → refine again until perfect.

Instant application

Non-streaming processing for speed. Results applied directly with conflict detection to protect your edits.

Common use cases

Clarify vague requirements before planning

Turn "fix the auth bug" into specific steps with edge cases, affected services, and validation checks.

Expand abbreviated notes into full context

Convert shorthand like "refactor user svc" into detailed task with constraints, dependencies, and rollback plan.

Refine task descriptions for team alignment

Ensure task descriptions are clear for reviewers and downstream implementation.

Frequently Asked Questions

Everything you need to know about PlanToCode

Yes. PlanToCode provides a human-in-the-loop workflow where team leads and stakeholders can review generated implementation plans, edit details, request modifications, and approve changes before they are executed by coding agents or developers. This ensures corporate governance and prevents regressions.

Upload Microsoft Teams meeting recordings or screen captures to PlanToCode. Advanced multimodal models analyze both audio transcripts (including speaker identification) and visual content (shared screens, documents) to extract specification requirements. You review the extracted insights - decisions, action items, discussion points - and incorporate them into implementation plans.

Yes. Implementation plans break down changes on a file-by-file basis with exact repository paths corresponding to your project structure. This granular approach ensures you know exactly what will be modified before execution, providing complete visibility and control.

Start capturing clear specifications with AI

Download PlanToCode to combine voice capture, video context, and inline rewriting before you generate implementation plans.

Watch the demo•See voice features