> ## Documentation Index
> Fetch the complete documentation index at: https://plantis.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Step 2: Idea Shaping

> Design and constraint exploration before implementation

**Goal**: Collaborate with the AI on design decisions before writing any code.

## Moving to "Idea Space"

The critical instruction to use:

> **"Don't make any code changes yet, just propose a plan"**

### Why This Matters

* Prevents premature implementation
* Keeps the AI in exploration mode
* Allows you to course-correct before code is written
* Reduces "helpful" refactors that break things

## Operating at Every Level

The senior engineer must guide the AI across **multiple architectural levels**:

### Level 1: Architecture

* How does this fit into the overall system?
* What modules/services are affected?
* Are there cross-cutting concerns?

### Level 2: Dependencies

* What existing code do we need to modify?
* What new dependencies might we need?
* Are there circular dependency risks?

### Level 3: Style Conventions

* What patterns does this codebase follow?
* Are there established conventions to match?
* What's the testing strategy?

### Level 4: Naming Details

* What naming conventions are used?
* How should new functions/variables be named?
* Does this match the existing vocabulary?

## Connecting the Dots

Instead of taking a "leap of faith" with a confident-sounding AI, **ground suggestions in your own knowledge**:

### Ask for Alternatives

```
"What are 2-3 different approaches we could take here?
What are the trade-offs of each?"
```

### Request Pros and Cons

```
"You suggested using Redis for caching.
What are the downsides? What alternatives exist?"
```

### Challenge Assumptions

```
"You assumed we need real-time updates.
Can we use polling instead? What would that look like?"
```

**Key insight**: The AI doesn't know your constraints—you must surface them through questions.

## Creating Spec Documents

Know when to move a long chat-based plan into a **durable spec.md file** for larger features.

### When to Create a Spec

* Feature will take >1 day to implement
* Multiple engineers need to understand the plan
* The design needs stakeholder review
* You'll need to reference it across sessions

### Spec Template

```markdown theme={null}
# Feature: [Name]

## Problem
What problem does this solve?

## Solution
High-level approach

## Architecture
How does this fit into the system?

## Implementation Plan
1. Step 1
2. Step 2
3. Step 3

## Edge Cases
What could go wrong?

## Testing Strategy
How will we verify correctness?
```

**Pro tip**: Ask the AI to generate the spec from your conversation, then review and refine it.

***

**Key Principle**: Design in "idea space" with full flexibility. Lock it in before moving to "code space" where changes are expensive.