(1) Using AI to code programs. Increasingly, AI is used to write traditional applications. This has many in the technical community worried about future job preservation as well as the capability of debugging AI-developed code that produces strange results. See
AI anxiety.
(2) Writing an AI application. AI programming means creating a neural network in Python, C++ or Java, which is the first phase in developing an AI machine learning model. AI engineers determine the number of layers, neurons and passes through the model, all of which are known as "hyperparameters." This is the first phase in developing an AI application. See
AI training vs. inference and
hyperparameter.
AI Can Assist in Part or in Full
As of 2025, the overwhelming majority of neural networks are programmed by humans; however, AI can assist in programming some of the network code, and in some cases, completely code the neural network. That means AI is writing AI, but as of 2025, this is very limited. See
neural network,
AI training and
AI programming languages.
Programming the Inference Engine
Inference engines, which comprise the software that users work with, employ the models created by the neural network to generate answers. The inference engine must also be programmed, and inference programming is considered as hard core as it gets in the AI world. Programmers must be proficient not only in low-level languages and computer architecture, but in math, compilers, deep learning frameworks and hardware interfaces such as Tensor and CUDA.
After comparing regular program development with AI development in the following outline, it should be very obvious that AI creation is another ballgame!
REGULAR DATA PROCESSING DEVELOPMENT:
1. design the logic
2. code the logic
3. test application
4. run application
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AI NEURAL NETWORK DEVELOPMENT:
Model Development
1. INITIAL DESIGN
1a. select network type (CNN, RNN, GAN, etc.)
1b. code the model
1c. set layers, neurons, passes (hyperparameters)
1d. software sets weights and biases (parameters)
2. PRE-TRAIN (with example data sets)
2a. hyperparameters mostly adjusted by people
2b. parameters adjusted by software
3. FINE-TUNE (with example data sets)
3a. hyperparameters mostly adjusted by people
3b. parameters adjusted by software
See AI hyperparameter.
Inference Engine
1. design
2. code
3. optimize (see AI quantization)
Execute AI Application
1. run inference engine with built-in model
or
2. run inference engine and select model