John

Claude Code's Output Styles: Breaking Free from AI Dependency with Learning Mode

August 22, 2025

I was reviewing a pull request when I realized something troubling: I couldn’t explain how the code worked. Sure, Claude Code had generated it beautifully - clean, well-structured, following all the best practices - but I had become a passive observer in my own development process. I could approve the PR, but I couldn’t confidently modify or debug the logic if needed.

That moment of realization led me to discover Claude Code’s most underutilized features: Learning Mode and Insights Mode. These output styles fundamentally changed how I interact with AI assistance, transforming me from a passive consumer back into an active learner and participant in the development process.

The Passive AI Dependency Problem

Most developers using AI tools fall into a comfortable but dangerous pattern:

1. Describe what you want
2. AI generates the code
3. Test if it works
4. Ship if it passes tests
5. Move to the next feature

This workflow is incredibly efficient in the short term, but it creates subtle long-term problems:

• Skills atrophy - You stop thinking through implementation details
• Knowledge gaps - You can’t debug or modify AI-generated code confidently
• Architecture blindness - You lose sight of why certain patterns were chosen
• Learning stagnation - You stop building new mental models and understanding

I had become skilled at prompting AI but was losing touch with the fundamental problem-solving skills that made me a good developer in the first place.

Enter Output Styles: A Different Approach

Claude Code’s output styles solve this by changing the fundamental interaction model. Instead of “generate code for me,” it becomes “help me learn while we build this together.”

Learning Mode: The Collaborative Teacher

Learning Mode transforms Claude from a code generator into a collaborative partner. When activated with /output-style learning, it:

• Explains implementation choices as it works
• Asks you to contribute strategic pieces of code
• Adds TODO(human) markers for you to implement
• Shares insights about patterns and architectural decisions

Here’s what a typical Learning Mode interaction looks like:

# Claude generates the structure and explains the approach
class UserService:
    def __init__(self, db_connection):
        self.db = db_connection
        # TODO(human): Initialize the cache layer here
        # Think about what cache backend would work best for user data
        
    def get_user(self, user_id):
        # First, let's check the cache for this user
        # TODO(human): Implement cache lookup logic
        
        # If not in cache, fetch from database
        user = self.db.fetch_user(user_id)
        
        # TODO(human): Add the user to cache before returning
        # Consider: what should the TTL be for user data?
        
        return user

The magic happens in those TODO(human) sections. Instead of receiving complete code, you’re prompted to think through specific decisions:

• What caching strategy makes sense?
• How long should user data stay cached?
• What happens if the cache is unavailable?

This keeps your brain engaged in the architectural decisions while Claude handles the boilerplate and explains the patterns.

Insights Mode: The Technical Narrator

Insights Mode (activated with /output-style explanatory) focuses on education through detailed explanation. It provides “educational insights” about implementation choices and codebase patterns.

When refactoring a complex function, Insights Mode might explain:

// Converting this to use async/await instead of promises
// Insight: async/await is more readable but has subtle differences
// in error handling - we need try/catch blocks for each await
async function processUserData(userId) {
    try {
        const user = await fetchUser(userId);
        // Insight: We're fetching permissions separately to avoid
        // a complex join query that could timeout with large datasets
        const permissions = await fetchUserPermissions(userId);
        
        return {
            ...user,
            permissions
        };
    } catch (error) {
        // Insight: Specific error handling here prevents exposing
        // internal system details to the client
        throw new UserProcessingError('Failed to process user data');
    }
}

The insights explain not just what the code does, but why specific approaches were chosen over alternatives.

The Mental Model Shift

The key difference between standard mode and these output styles is the mental model they promote:

Standard mode creates a cycle where you become increasingly dependent on AI assistance. Learning and Insights modes create a cycle where AI assistance makes you more capable over time.

Practical Applications

When to Use Learning Mode

Learning Mode works best when:

• Learning new frameworks or languages - Forces you to understand core concepts
• Complex architectural decisions - Ensures you understand trade-offs
• Debugging unfamiliar codebases - Builds understanding of existing patterns
• Pair programming sessions - Maintains active participation in problem-solving

When to Use Insights Mode

Insights Mode excels for:

• Code reviews and analysis - Understand why code is structured a certain way
• Legacy code modernization - Learn about outdated patterns and modern alternatives
• Performance optimization - Understand bottlenecks and solution approaches
• Documentation and knowledge transfer - Generate explanations for team members

Combining with Standard Mode

The most effective approach is mixing modes based on your learning goals:

• Standard mode for repetitive, well-understood tasks
• Learning mode for building new skills or tackling unfamiliar problems
• Insights mode for understanding existing systems or complex patterns

The Long-Term Impact

After using these modes consistently for several months, I noticed significant changes in my development approach:

Improved Problem-Solving

Instead of immediately reaching for AI assistance, I now think through problems first. The Learning Mode habit of contributing strategic pieces made me more confident in my own reasoning abilities.

Better Code Reviews

Insights Mode trained me to think about the “why” behind code decisions. My code reviews now focus on architectural choices and trade-offs, not just syntax and style.

Faster Debugging

Understanding how code works (instead of just accepting that it works) made me much more effective at debugging issues. I can trace through logic confidently and identify potential failure points.

Knowledge Retention

The biggest surprise was how much better I retained new concepts. When you actively participate in building solutions, the knowledge sticks in ways that passive consumption never achieved.

Implementation Tips

Start Gradually

Don’t switch to Learning Mode for every task immediately. Begin with:

• New technology explorations
• Complex features you want to understand deeply
• Code reviews of unfamiliar systems

Embrace the Friction

Learning Mode is intentionally slower than standard mode. This “friction” is the feature, not a bug. The extra time spent understanding pays dividends later.

Save the Insights

Both modes generate valuable explanations and reasoning. I save these insights to my project’s CLAUDE.md file or personal knowledge base for future reference.

Mix with Visual Learning

Combine these modes with diagram generation. Ask for Mermaid diagrams to visualize the concepts being explained - the visual + collaborative approach is incredibly powerful.

Key Learnings

• AI dependency is a real risk - Standard mode can make you passive and reduce your problem-solving skills over time
• Learning Mode keeps you engaged - TODO(human) markers force active participation in the development process
• Insights Mode builds understanding - Explanations of “why” are more valuable than just seeing “what”
• Friction can be valuable - Slower AI interactions that require thinking lead to better learning outcomes
• Knowledge compounds differently - Active participation creates stronger mental models than passive consumption
• Mix modes strategically - Use standard mode for known tasks, learning modes for skill development
• Save the insights - Document the explanations and reasoning for future reference
• Visual + collaborative learning - Combine output styles with diagram generation for maximum understanding

The biggest realization was that optimizing for immediate productivity might be counterproductive in the long run. Learning Mode and Insights Mode sacrifice some short-term efficiency to build long-term capability. They transform AI from a crutch that makes you weaker into a teacher that makes you stronger.

If you’ve been using AI tools primarily in “standard mode,” try switching to Learning or Insights mode for your next complex task. The difference in engagement and understanding might surprise you.