In the history of technological breakthroughs, Generative Pre-trained Transformers (GPT) stand out as a monumental leap in Artificial Intelligence, with the potential to fundamentally transform the way we, as Developers, work.

This highly advanced and sophisticated AI Language Model offers a plethora of ground-breaking software engineering applications, ranging from code generation to automating complex, repetitive tasks. This article explores the concept of GPT, its various applications, limitations, and tips for optimal utilization in the context of Software Engineering.

What is GPT?

GPT, or Generative Pre-trained Transformer, is a Machine Learning model which utilizes Deep Learning techniques to produce human-like natural language text. It can be applied to a wide range of tasks, such as answering intricate questions within context, summarizing text, code generation, language translation, as well as numerous other applications.

GPT-3.5: The current version of GPT, GPT-3.5, is based on a dataset of billions of webpages, books, and text-based information (up until 2021), and contains 175 billion parameters.

GPT-4: The next release of GPT, GPT-4, is anticipated to feature a vast dataset of trillions of webpages, books, and other textual sources, and is expected to contain over 100 trillion parameters.

How can GPT be used today?

There are numerous Tools on the market that are built on GPT Technology, and, from a Developer perspective, the following outlines those which are most likely to provide the best entry point for enhancing DX.

ChatGPT: The most common entry into GPT, ChatGPT is a language model that is trained on a massive amount of textual data. This allows it to generate human-like text and respond to a wide range of prompts with impressively high accuracy. Conceptually, ChatGPT can be thought of as a successor to traditional search in that it essentially cuts out the entire process of searching, identifying relevant results, following links to those results, sifting through content, and trying to arrive at an answer. GPT eliminates this by providing answers or relevant information directly in response to questions in a natural and intuitive manner.

GPT API: The GPT API allows developers to access GPT’s capabilities via a REST API. The API can be used to generate text, translate text, and answer questions. API access is based on a pay-per-use basis, with pricing dependent on the number of requests issued and the amount of text generated. A free tier for developers to test the API is also available, as well as custom pricing for enterprise customers with high volume usage.

GPT Playground: Similar to ChatGPT, yet fully configurable and more stable, the Open AI GPT Playground allows users to experiment with the full set of GPT’s capabilities, including Model selection, introspection, and much more.

Additional Tools built on GPT: There are far too many to tools available which are built on GPT to list within the scope of this short article, however a few notable mentions are the ChatGPT – Genie AI VSCode Plugin, as well as the OpenAI NPM Package.

How can GPT Enhance Developer Experience?

While there are numerous applications for which GPT Technology can be utilized to provide an enhanced Developer Experience (DX), below are is a brief summary of a few of the most common.

Unit Test Generation: GPT can be used to generate test cases and setup, allowing developers to expedite the process of test setup, configuration, and initial test cases.

Debugging: GPT can be utilized to help debug issues in source code, identify misconfigurations, and more.

Code Generation: GPT can be utilized to generate source code, examples for specific languages and frameworks, convert source code from one language to another, and much more.

Streamlining Workflows: GPT can be integrated into development tools, such as IDEs and issue tracking systems, to automate repetitive tasks and streamline workflows.

Technical Documentation: GPT can be utilized to generate technical documents, such as API docs, design specifications, and more, thus improving the quality and accuracy of the information available to developers and teams.

Automating Repetitive Tasks: GPT can be trained to handle repetitive tasks such as scheduling builds, deployments, responding to common queries and more, freeing up engineering developer’s time for more important tasks.

Streamlining Communication: GPT can be integrated into communication tools such as Jira, Teams, etc., allowing Developers to quickly and easily communicate with team members, saving time and improving efficiency.

Identifying Patterns and Trends: GPT can be leveraged to analyze large amounts of data, such as engineering analytics, project management information, etc. to identify patterns and trends that may be difficult for humans to detect, helping Teams to make informed decisions.

Current Limitations

As a relatively new Product, certain limitations and issues are to be expected as the platform matures, namely, they are as follows.

Error Prone: GPT is regularly prone to error, and in certain cases, once an error is encountered, the conversation cannot be continued, leaving one to have to start their prompts over again within a new chat.

Accuracy and Completeness: GPT’s accuracy and completeness is often quite limited, and so it is crucial that Developers be prudent in validating outputs. Moreover, as the Model’s dataset cutoff date was in 2021, not all prompt outputs are currently relevant.

User Experience: The ChatGPT UX is lacking in many areas and doesn’t quite do the underlying platform justice. The UI is often slow and a bit disjointed; however, when it is stable, it is certainly quite usable and helps to accomplish one’s goals – this is particularly true when using a Chat GPT Plus Account.

Tips and Considerations

As with any tool, it is crucial to have an understanding of it’s capabilities and best practices in order to get the most from the experience. A few mentionable items are as follows.

Utilize Prompt Engineering: Be specific and focus on one particular topic or aspect of a topic. Resist the urge to use polite expressions such as “please”, “thank you”, etc. Instead, focus on including the necessary input required to receive the desired output.

Provide Specific Context: The more specific the information you provide to the model, the better the output will be. This can be done by providing a clear and concise, yet very specific question, including the necessary context required for the task you want the model to perform. Likewise, be mindful of ethical considerations – do not interact with ChatGPT in an unethical manner.

Be Mindful of Sensitive Information: Inputs provided to ChatGPT should always be assumed to be persisted and potentially made publicly available. Do not provide any sensitive or proprietary information, such as usernames, passwords, keys, domain specifics, or business specifics.

Validate and Verify Output: Always make sure to validate and verify received output. Never use output directly without first vetting it for accuracy, completeness, etc.

Explore the Open API Playground: Once you are comfortable using ChatGPT, try the Open API Playground, as it provides low-level access to GPT, such as switching models, configuring token length, and numerous additional configurations.

Innovative Use-Cases

While it is inevitable that there will be countless applications for utilizing GPT technology in Software Development, the following outlines some exciting possibilities on the horizon.

Application Source Ingestion and Optimization: Utilizing GPT to ingest application source code provides significantly enhanced analysis. Such integrations can create a model of an application’s data and control flow and suggest opportunities for optimization, reactively identify issues, and generate comprehensive design documentation.

Automated Code Reviews: Integrating GPT as an NLP tool to perform automated code reviews based on organization and team best practices, industry best practices, and historical data from previous code reviews can streamline the process. This can be integrated directly within IDEs, significantly speeding up existing code review processes.

Application Integration: Integrating GPT within applications can streamline help documentation, how-to guides, and augment existing features, providing users with a more seamless experience.

Enhanced API Docs: Integration within platforms can optimize adoptability via enhanced API examples. For instance, a Swagger implementation where a user simply states what they are trying to do, and instantly receives a complete example, streamlining the development process.

Conclusion

GPT offers a transformative leap in Natural Language Processing, significantly impacting developers and engineering managers by streamlining workflows, automating repetitive tasks, and providing advanced capabilities in various aspects of software development. As the technology continues to evolve, it is essential for developers and engineering teams to stay informed about the latest developments, limitations, and best practices to make the most out of this powerful AI tool.