Generative AI in Software Development: The Beginner’s Guide

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When it comes to generative AI in software development, why is this the ultimate guide?

Our guide is:

  • Updated regularly to include new information on this rapidly-advancing topic
  • Vetted by AI experts with decades of experience in the field
  • Hype-free and realistic regarding generative AI’s business use cases

Now, let’s dive in to the topic.

What is Generative AI in Development?

A type of artificial intelligence, generative AI models can whip up new data samples that bear a striking resemblance to the original training data. This capability can be applied to a host of tasks such as sprucing up existing data, churning out content, or detecting anomalies in a dataset.

Generative models work by learning the underlying patterns and structures in a large dataset.

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Types of Generative Models

While ChatGPT and Copilot may be the most discussed generative AI models, there are several different types.

  • GANs (Generative Adversarial Networks) consist of two neural networks: a generator and a discriminator. The generator learns to create realistic data samples by generating fake samples, while the discriminator learns to distinguish between the real and fake samples. Through this process of competition, the generator is forced to improve its output to generate samples that are more and more realistic. GANs can be used for tasks such as generating realistic images, video, and music.


  • VAEs (Variational autoencoders) work by encoding input data into a lower-dimensional space, and then decoding it to generate new data. The encoder learns to compress the input data into a smaller, latent space, while the decoder learns to reconstruct the input data from the latent space. In simple terms, you can think of latent space as a compressed, abstract representation of the original data (typically images or music), and the lower-dimensional space as a way to represent the original data in a simpler, easier-to-process format.


  • Autoregressive models work by predicting the probability distribution of each token in a sequence, given the previous tokens. The model learns to predict the next token in a sequence based on the previous tokens. This allows the model to generate new sequences of data that are similar to the training data. Autoregressive models are commonly used for generating natural language text, such as in language translation or text completion tasks.

How Generative Models Work

Generative AI can either produce outputs for you based on a natural language input, or it can analyze an input (like a code snippet) and optimize it, or suggest improvements.

Thus, the outputs are only as strong as the inputs, and the parameters you put into place will determine their quality and alignment with your goal.

For software development, generative AI is an incredible tool that can be used for:

  • Software Coding
  • Software Testing
  • Code Verification
  • Automation
  • Project Management
  • Idea Generation
  • Documentation

And new use cases are being explored every day.

Why Do You Need Generative AI in Software Development?

For many software development teams, generative AI is posing a critical question: “If we don’t implement generative AI now, how far behind will we be?”

This question is a legitimate one.

Generative AI can significantly boost productivity and enhance code quality. One developer can accomplish 10x more with the help of generative AI—an appealing prospect that can lead to a significant advantage over competitors in the market.

Though it’s important to implement generative AI thoughtfully, with the appropriate precautions and ethical considerations, every software company must reckon with this rapidly-changing tech, or risk being left behind.

Generative AI Development Terms

  • Generative models: Machine learning models that can generate new data that is similar to the training data it has been given.


  • Large language models (LLM): A type of generative model, like ChatGPT, that is trained on vast amounts of text data to generate new text that is coherent and grammatically correct.


  • Generative Pre-trained Transformer (GPT): A family of LLMs developed by OpenAI that are trained on vast amounts of text data using a transformer-based architecture. These models can generate new text that is coherent and relevant to a given input prompt, and have been used for a variety of natural language processing tasks such as language translation, text completion, and question answering. ChatGPT, which is a language model that can generate text in response to user prompts, is an example of a GPT-based model.


  • Natural language processing (NLP): A field of study in computer science and artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language.


  • Hallucinations: In the context of generative models, hallucinations refer to generated data that is completely outside the scope of the training data and is not reflective of any real-world phenomenon. In other words, hallucinations are often false and can be indistinguishable from the truth


  • Attention: A mathematical process used in many natural language processing (NLP) models, such as machine translation and text summarization, that allows the model to process snippets of text in parallel. The basic idea behind attention is to selectively focus on different parts of the input sequence during the encoding and decoding process, by assigning weights to different parts of the input sequence based on their relevance to the current task.


  • Style transfer: Style transfer is a technique that can be used with generative models, such as GANs, to transform the style or appearance of an input image or video while preserving the content. While ChatGPT is not specifically designed for style transfer, it could be used in conjunction with other models to generate text descriptions of style-transformed images or videos.


  • Adversarial training: Adversarial training is a technique used to train generative models, such as GANs, to generate data that is similar to the training data, while also enforcing a distributional constraint on the generated outputs. While ChatGPT is not specifically designed for adversarial training, the same technique could be used to train a large language model like ChatGPT to generate text that is similar to a given style or tone.

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