Teaching AI to Write Bad Code — On Purpose

Why training AI to be imperfect might be the key to creativity and human-like thinking

🧠 The Counterintuitive Approach That's Changing AI

Imagine teaching a student to be deliberately wrong. It sounds backwards, doesn't it? Yet this is exactly what some AI researchers are doing with code-writing artificial intelligence. They're teaching AI systems to write "bad" code on purpose, and the results are surprising everyone.

This approach, called adversarial training or imperfection injection, is revealing something profound about how creativity and human-like problem-solving actually work. Let's dive into why making AI worse at coding might actually make it better at thinking.

⚠️ The Problem with Perfect AI

Traditional AI training follows a simple rule: reward correct answers, penalize wrong ones. When training AI to write code, we typically feed it millions of examples of clean, efficient, well-structured programs. The AI learns to mimic these patterns perfectly.

But here's the catch: humans don't write perfect code. We make mistakes, we experiment, we take detours, and sometimes we solve problems in roundabout ways that somehow work better than the "correct" approach.

When AI only learns from perfect examples, it becomes like a student who only knows textbook solutions. It can solve standard problems beautifully, but struggles when faced with creative challenges or novel situations that require thinking outside the box.

🤔 What Does "Bad Code" Actually Mean?

Before we go further, let's clarify what we mean by "bad code." We're not talking about code that doesn't work or causes security vulnerabilities. Instead, we're referring to code that:

Think of it like learning to paint. If you only studied perfect masterpieces, you'd never understand the sketches, the experiments, the "happy accidents" that led to breakthrough techniques.

🧪 The Science Behind Imperfect Training

Step 1: Collecting Messy Data

Traditional AI training uses curated datasets filled with polished code examples. The new approach intentionally includes:

Researchers collect this data from real programming sessions, GitHub commit histories, and collaborative coding environments where they can see the full problem-solving process.

Step 2: Teaching Pattern Recognition

Instead of just learning "this is the right answer," the AI learns to recognize patterns like:

The AI builds a more nuanced understanding of problem-solving that includes the messy, human process of exploration and discovery.

Step 3: Encouraging Exploration

During training, the AI is rewarded not just for finding correct solutions, but for:

This creates an AI that thinks more like a human programmer who experiments and adapts.

🔍 Real-World Examples: When "Bad" Code Leads to Better Solutions

Example 1: The Roundabout Route

A traditional AI might solve a sorting problem with a standard quicksort algorithm:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

An AI trained on "imperfect" examples might first try several approaches:

# Attempt 1: Bubble sort (inefficient but educational)
def sort_attempt1(arr):
    for i in range(len(arr)):
        for j in range(len(arr)-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

# Attempt 2: Custom approach that reveals insight
def sort_attempt2(arr):
    # What if we group by ranges first?
    small = [x for x in arr if x < 50]
    medium = [x for x in arr if 50 <= x < 100]
    large = [x for x in arr if x >= 100]
    # Then sort each group...

This process reveals that the AI is thinking about the problem from multiple angles, potentially discovering domain-specific optimizations that a perfectly trained AI might miss.

Example 2: The Creative Workaround

When asked to find duplicate elements in a list, a traditional AI gives the standard solution:

def find_duplicates(lst):
    seen = set()
    duplicates = set()
    for item in lst:
        if item in seen:
            duplicates.add(item)
        else:
            seen.add(item)
    return list(duplicates)

An "imperfectly" trained AI might show this thought process:

# First thought: Let's count everything
from collections import Counter
def find_duplicates_v1(lst):
    counts = Counter(lst)
    return [item for item, count in counts.items() if count > 1]

# Wait, what about using dict comprehension?
def find_duplicates_v2(lst):
    return list({x for x in lst if lst.count(x) > 1})

# Hmm, that's inefficient. What about...
def find_duplicates_v3(lst):
    return list(set([x for i, x in enumerate(lst) if x in lst[:i]]))

This exploration reveals different ways of thinking about the problem and might uncover edge cases or performance characteristics that matter in specific situations.

🎁 The Surprising Benefits

Enhanced Creativity

AI systems trained this way show remarkable creativity in problem-solving. They don't just find a solution; they explore the solution space and often discover novel approaches that human programmers find genuinely useful.

Better Error Handling

Because they've seen and learned from imperfect code, these AI systems are better at:

More Human-Like Collaboration

When working with human programmers, these AI systems feel more like collaborative partners than perfect but inflexible tools. They can engage in the messy, iterative process that real software development requires.

Robust Learning

Traditional AI can break down when faced with problems slightly different from their training data. AI trained on imperfect examples shows more robust learning—they've seen how to adapt and recover from approaches that don't quite work.

🛠️ The Technical Implementation

Data Collection Strategy

Researchers use several techniques to gather "imperfect" training data:

  1. Version Control Mining: Analyzing Git histories to see how code evolved through multiple commits
  2. Live Coding Sessions: Recording programmers working through problems in real-time
  3. Competitive Programming: Collecting multiple solutions to the same problem with different efficiency profiles
  4. Student Code Analysis: Using submissions from programming courses that show learning progression

Training Methodology

The training process involves:

  1. Multi-objective Optimization: Instead of optimizing for just correctness, the AI optimizes for correctness, creativity, explanation quality, and solution diversity
  2. Curriculum Learning: Starting with simple problems and gradually introducing more complex scenarios where multiple approaches are valid
  3. Reinforcement Learning: Rewarding the AI not just for final answers but for the quality of its problem-solving process

Evaluation Metrics

Success is measured by:

🚧 Challenges and Limitations

The Balance Problem

The biggest challenge is finding the right balance. Too much emphasis on imperfection, and the AI becomes unreliable. Too little, and it remains rigid and uncreative.

Researchers solve this by using weighted training objectives where correctness still carries the most weight, but creativity and process quality also contribute to the AI's learning signals.

Quality Control

There's a risk that teaching AI to write "bad" code could lead to genuinely problematic outputs. Safeguards include:

Computational Costs

Training AI to explore multiple approaches is computationally expensive. Each problem might require generating and evaluating dozens of potential solutions instead of just finding one correct answer.

🌐 The Broader Implications

Rethinking AI Training

This approach is causing researchers to reconsider how we train AI systems across many domains. The principle—that learning from imperfection can lead to more robust and creative intelligence—applies beyond coding to:

The Future of Human-AI Collaboration

As AI systems become more human-like in their thinking processes, they become better collaborative partners. Instead of providing perfect but inflexible solutions, they can engage in the messy, iterative process of real-world problem-solving.

Understanding Intelligence Itself

Perhaps most importantly, this research is teaching us something fundamental about intelligence. The ability to make mistakes, learn from them, and explore multiple approaches isn't a bug in human thinking—it's a feature. It's what allows us to be creative, adaptive, and innovative.

🚀 Getting Started: A Simple Example

Want to see this in action? Here's a simple example you can try:

Instead of asking an AI: "Write a function to reverse a string"

Try asking: "Show me three different ways to reverse a string, including one that might be inefficient but educational, and explain the thinking behind each approach"

The difference in response quality and educational value is remarkable.

🛤️ The Road Ahead

The field of "imperfect AI training" is still young, but early results are promising. We're seeing AI systems that are not just more capable, but more human-like in their problem-solving approaches.

This isn't about making AI worse at coding—it's about making AI better at thinking. By embracing the messy, imperfect, creative process that characterizes human intelligence, we're building AI systems that can truly augment human capabilities rather than simply replacing them.

The next time you see an AI make a "mistake" or take a roundabout approach to a problem, remember: that imperfection might be exactly what makes it more intelligent, more creative, and more useful as a thinking partner.

In the end, teaching AI to write bad code isn't about celebrating failure—it's about understanding that the path to intelligence isn't always a straight line. Sometimes, the most interesting discoveries happen when we're willing to take the scenic route.

---

The future of AI isn't perfect systems that never make mistakes—it's intelligent systems that can learn, adapt, and create alongside us. And sometimes, that means learning to be beautifully, purposefully imperfect.

Bye! 👋