Why you must spend money on project planning before writing code

Many developers make the mistake of jumping directly into writing code with AI agents. They treat the prompt like a magic wand, asking for entire applications in a single shot. This approach inevitably fails on complex, real-world tasks.

Structuring an unstructured client specification into concrete development phases before generating a single line of code changes the entire dynamic. When you build a highly detailed plan first using a premium frontier model, you can run the actual code implementation using faster, cheaper models like DeepSeek or Composer. The upfront investment in a premium model pays for itself by preventing downstream execution errors.

Setting Up the Local Workspace and Tech Stack

Before initiating a planning prompt, you must establish your project's local environment. This gives the AI agent immediate context regarding your directory structures and chosen frameworks.

For a Laravel web application, you can use the official installer to configure your foundation. Running the installer with the laravel-boost package automatically generates critical context files:

• Claude.md: Defines framework rules, coding styles, and context limits.
• agents.md: Lists framework-specific developer tools, CLI commands, and directory layouts.

Once the framework is ready, save the raw client requirements in a dedicated documentation folder (e.g., docs/project_description.md) and initialize a Git repository to track changes.

Executing the Planning Prompt with Claude Code

With your workspace configured, run Claude Code to analyze your documentation and compile the development phases. Switch the terminal utility into "high effort" mode to utilize Claude Opus.

# Initialize Claude Code session with High Effort enabled
claude --high-effort

Provide a comprehensive prompt instructing the agent to parse your project description, raise clarifying questions, and export a structured plan into docs/project_phases.md containing specific verification tests for every task.

# Example Planning Prompt Structure

Review the client specifications in docs/project_description.md.
Identify any ambiguous requirements and prompt me for clarification.
Generate a step-by-step implementation plan including:
- Database schemas and migrations
- Backend business logic components
- UI components
- Exact unit/feature tests required to verify each task

Output the final structure in markdown format to docs/project_phases.md.

Handling Clarification Questions in the Terminal

Claude Code offers an interactive user-prompt interface for answering questions during execution. Instead of halting or making assumptions, the terminal UI presents a structured, navigable panel listing critical questions.

Answer each requirement question carefully. If you do not have immediate answers, pause the CLI, export the list of questions for your client, and resume the planning phase once you have confirmed their preferences.

Syntax Notes: Structuring Acceptable Test Criteria

The generated docs/project_phases.md file should exceed 500 lines of highly detailed, actionable markdown. Pay close attention to how the agent structures acceptance criteria for individual tasks:

### Task 1.2: Users Table Migration
- **Objective**: Create the users database schema.
- **Testing Criteria**:
  - Assert `email` field is unique in database
  - Verify password hash is applied via Eloquent mutator

Defining explicit verification tests within the task specifications ensures that whatever cheaper model you select for the implementation phase knows exactly how to prove its code works before presenting it for human review.

Managing Your Resource Budget during Planning Runs

Executing high-effort planning runs on premium models consumes a measurable portion of your LLM platform quotas. A single comprehensive planning session typically uses about 7% of an Anthropic $20 monthly subscription tier, equivalent to roughly $1 in direct API billing. This small cost is highly efficient, saving hours of manual debugging and reducing the total token count needed for subsequent development runs.