LLMs as Rubber Ducks: Rethinking AI's Role in Development

Thinking With AI

Like many developers, I initially dismissed LLMs as glorified code generators. I watched colleagues copy-paste solutions from ChatGPT, creating what I call "zombie codebases" - they walk and talk, but there's no soul inside. No understanding. No intentionality.

Then something changed. During a particularly frustrating debugging session, I found myself explaining my problem to ChatGPT. Not asking for a solution, but thinking out loud about the architecture. The responses were thoughtful questions that challenged my assumptions and exposed blind spots in my thinking.

That's when I realized: The true power of LLMs are intelligent conversation partners that can help us think through complex problems - much like the classic rubber duck debugging method, but with the ability to challenge our assumptions and push our thinking further.

The Rubber Duck Revolution

For those unfamiliar with rubber duck debugging, it's a method where you explain your code line by line to a rubber duck. The act of articulating your problem often leads to discovering the solution. It's surprisingly effective, but it has limitations - your rubber duck never challenges your assumptions.

This is where LLMs come in. They're like rubber ducks that:

• Ask probing questions
• Challenge your assumptions
• Suggest alternative perspectives
• Point out potential issues you haven't considered

But here's the crucial part: they do this without writing a single line of code for you.

My Golden Rules

After months of refining my approach, I've developed three fundamental rules:

1. Never Ask for Direct Solutions

Instead of "How do I implement X?", I ask "What should I consider when implementing X?" or "What are the potential pitfalls of approach Y?"

2. Use LLMs as Thought Partners

I discuss architecture, debate trade-offs, and explore edge cases. The implementation remains my responsibility.

3. Different Models for Different Conversations

   Not all LLMs are created equal.

   The key is understanding these differences. For example:

   • Some models are better at maintaining long-term context
   • Others are more precise with technical details
   • Some excel at creative problem-solving
   • Others are better at systematic analysis

   By matching the model to the type of conversation I want to have, I get much better results.

Why Rubber Ducks?

The rubber duck analogy is fundamental to understanding how to use LLMs effectively. Here's why:

The Power of Articulation

When you explain a problem to someone (or something), you're forced to:

• Break down complex issues into digestible pieces
• Question your assumptions
• Find gaps in your logic
• Discover solutions you overlooked

But LLMs take this further. They're interactive rubber ducks that can spot patterns in your thinking and challenge your blind spots.

The Hallucination Problem

Let's talk about the elephant in the room: LLM hallucinations. I've seen developers blindly trust AI-generated code, leading to:

• Security vulnerabilities from imagined API parameters
• Performance issues from non-existent optimizations
• Integration bugs from hallucinated library features

This is why I never copy code directly from LLMs. Instead, I use them to:

• Validate my approach
• Explore potential edge cases
• Question my assumptions
• Guide my research

Context Is King

One of the most powerful aspects of using LLMs as rubber ducks is their ability to maintain context. But this is also where you need to be careful.

Managing Context Effectively

1. Start Broad, Go Deep

   ❌ "How do I fix this bug?"
   ✅ "I'm working on a Node.js microservice that handles..."
   

2. Build Context Incrementally

   • Start with system architecture
   • Add specific component details
   • Describe the current problem
   • Explain your attempted solutions

3. Verify Understanding

   Have the LLM summarize its understanding before proceeding with detailed discussions.

Security Considerations

Data Protection

Never share:

• API keys or secrets
• Production database schemas
• User data or PII
• Internal security measures

Instead, abstract sensitive information:

# ❌ Don't share
api_key = "ak_live_12345..."

# ✅ Do share
api_key = "API_KEY_PLACEHOLDER"

Code Security

When discussing security-related code:

1. Use pseudocode for sensitive logic
2. Focus on patterns rather than implementation
3. Verify suggestions against security best practices
4. Never trust security advice without verification

Advanced Conversation Patterns

The Art of Asking Questions

After spending countless nights debugging ML pipelines and managing distributed systems, I've learned something crucial: the quality of your questions determines the quality of your solutions. When I first started using LLMs, I made the same mistake as everyone else - asking for quick fixes. Now, I treat them as architectural thought partners, and here's how:

Let me break down my questioning strategy that consistently produces results:

1. Start with System Context

   "Our recommendation engine handles 50M daily predictions across 3 regions.
   Current architecture:
   - Real-time feature computation on Spark
   - Model serving via TensorFlow Serving
   - Multi-stage caching layer
   
   We're seeing latency spikes during feature computation.
   Let's analyze potential bottlenecks before jumping to solutions."
   

   This sets the stage for meaningful discussion. The LLM now understands the scale, complexity, and specific area of concern.

2. Explore Decision Boundaries

   "We're considering two approaches for our feature store:
   1. Redis + Time-series DB combination
   2. Custom solution built on PostgreSQL
   
   Key requirements:
   - Sub-5ms read latency
   - Support for vector operations
   - Real-time feature updates
   
   What critical factors am I missing in this trade-off analysis?"
   

   By presenting my current thinking, I get targeted insights rather than generic solutions.

3. Challenge Core Assumptions

   "Current assumptions about our ML pipeline:
   - Batch processing is sufficient (4h window)
   - Data consistency > Availability
   - Cold starts are acceptable
   
   Which of these assumptions might break at 100x scale?"
   

   This forces both me and the LLM to think about fundamental limitations.

The key is to treat these conversations like high-level architectural discussions. I'm not looking for code - I'm pressure testing ideas and uncovering blind spots. This approach has helped me catch critical design flaws early and build more resilient systems.

The Reality Check

Look, I get it. Sometimes you're under pressure, the production system is down, and management is breathing down your neck. In those moments, you'll probably copy that quick fix from ChatGPT. And you know what? That's fine.

But here's where most developers make a critical mistake: they close the chat window as soon as they get their code working.

Don't do that.

Instead, once your fire is out, take a moment to understand what you just implemented. Not because of some abstract ideal about "learning," but for a very practical reason: you'll probably have to maintain or debug this code later.

Here's what I do after using a quick fix:

1. Ask for an explanation

   "I understand this fixed my issue, but can you explain:
   - Why this solution works
   - What potential side effects it might have
   - What assumptions it makes about the system"
   

2. Understand the context

   "In what scenarios might this solution break?
   What should I watch out for in production?"
   

3. Document for future you

   "What would be the key points to include in a comment
   to help someone (including future me) understand this fix?"
   

Think of it as an investment in your future sanity. The 5 minutes you spend understanding a solution now might save you hours of debugging later.

Remember: Using AI for quick fixes isn't a sin. Not understanding what you're putting into production is.

Conclusion

LLMs aren't magic - they're tools. Like any tool, their value comes from how you use them. By treating them as intelligent rubber ducks rather than code generators, we maintain control while leveraging their capabilities.

Remember:

• Question everything
• Understand deeply
• Implement thoughtfully
• Validate rigorously

The future of development isn't about letting AI write our code - it's about having smarter conversations about the code we write.