Creating a neural network with ChatGPT: practical experience and mistakes

Hands-On Experience: Building a Neural Network with ChatGPT and What Went Wrong

Developers often wonder if modern language models can replace programmers. Our real-world experiment creating a neural network using ChatGPT revealed that reality is more nuanced than expected—code generates quickly, but demands expert scrutiny.

First Steps: From Idea to Prompt

The project kicked off with an unusual request: a friend dreaming of stock market predictions got AI advice to use a neural network for analyzing quotes. This absurd chain—neural network suggesting another neural network—sparked our technical experiment. We set out to test if ChatGPT could independently write working code for a simple fully connected neural network.

Using an early model version, we crafted a detailed prompt with requirements:

• Multi-layer perceptron implementation
• Forward and backward propagation functions
• Training via gradient descent
• Support for arbitrary layer architectures

Analyzing the Generated Code: Brilliance Mixed with Bugs

Initial impressions were mixed. The AI grasped the overall structure but made fundamental implementation errors. Key issues uncovered in our review:

• Incorrect matrix operations in the feed-forward function
• Duplicate logic scattered across code sections
• Inefficient activation function calculations
• No handling of edge cases

The most telling error was in matrix multiplication. ChatGPT produced:

dot_product = np.dot(weights_i, layers_i)

With correct dimensions for weights_i (n_in, n_out) and layers_i (n_in,), this threw an exception. The fix was a simple but crucial tweak—transposing the weights matrix:

dot_product = np.dot(weights_i.T, layers_i)

Debugging Process: From Auto-Generation to Manual Fixes

After several prompt iterations, it became clear: ChatGPT acts like a C-student mechanically stitching code snippets without deep understanding. We systematized our AI-assisted coding approach:

Key checks for AI-generated neural network code:

• Dimensional correctness in all matrix operations
• Accurate gradient computation in backpropagation
• Efficient activation function implementations
• Handling diverse network architectures
• Quality of weight initialization

Experiment Results: Nier Library (Automata Ver1.1a)

After fixing core bugs, we had a working implementation packaged as a Python library. Key features:

• Networks with arbitrary layer counts
• Sigmoid activation support
• Stochastic gradient descent training
• Examples with classic datasets

The library was tested on standard tasks:

• Iris flower classification
• Temperature conversion regression
• Tic-tac-toe game

Technical Takeaways for Developers

Our experience proves ChatGPT and similar tools are powerful assistants, not standalone developers. Main lessons:

AI code generation strengths:

• Rapid solution skeletons
• Alternative approach suggestions
• Documentation and comments
• Boilerplate code

Critical limitations:

• Lacks deep contextual understanding
• Mechanically combines patterns
• Can't handle complex debugging
• Risks plausible-but-broken code

Bigger Picture: Neural Networks in the Age of Tech Singularity

We ran this experiment amid AI hype and the release of the Nier: Automata anime, which explores machine consciousness. These parallels aren't coincidental—neural network advances raise technical and ethical questions for developers.

Modern models impress, but their code "hallucinations" highlight core limits. AGI remains distant; current systems need constant human oversight.

Practical Tips for Teams

From our experience, here's a workflow for using AI in development:

• Clear requirements—detailed prompts with examples
• Step-by-step generation—break tasks into small pieces
• Mandatory code review—expert line-by-line checks
• Comprehensive testing—validate across scenarios
• Document fixes—analyze common AI errors

The Future of AI-Assisted Development

Code generation tech will improve, but developers' roles evolve, not vanish. Critical thinking, algorithm knowledge, and deep debugging become premium skills.

Our neural network-via-neural-network experiment shows: the real value isn't Python lines, but human expertise turning raw output into reliable solutions.

Key takeaways:

• AI-generated code needs as much review as human code
• Matrix operation errors are common in AI neural nets
• Mid/senior developers remain essential for validation and debugging
• Modern AI tools assist, don't replace programmers
• Prompt quality directly impacts generated code usefulness

— Editorial Team