New breakthrough AI systems are making headlines for good reason.

The ability to accelerate software development, close skill gaps and even improve overall code quality all present extraordinary potential value. Having said that, AI tools are just that: tools. Particularly when building mission-critical digital products and experiences, these tools are only as effective as they are reviewed, refined and continuously realigned toward target outcomes and objectives.

So if you’re a developer looking for practical ways to integrate AI into your workflow, you’ve come to the right place. We designed this guide to demonstrate what we think are the most fruitful and feasible places to start.

GITHUB COPILOT

GitHub Copilot is an AI-powered tool that developers can use directly within code editors.

When utilized properly, it can dramatically accelerate development, improve code quality, and even help build new skill sets and programming language fluency.

Here’s how you can put it to work today.

1. Intelligent Code Completion: Because GitHub Copilot can understand the context of existing code, it automatically suggests accurate and relevant code completions in real-time.

2. Generate Boilerplate Code: It’s the shortcut developers have always longed for. GitHub Copilot can quickly generate things like code templates, class definitions and function signatures, so repetitive coding tasks don’t prevent you from focusing on the core logic of your application.

3. Integrate External Libraries and APIs: When working with unfamiliar libraries or APIs, GitHub Copilot can accelerate your learning curve. With a simple prompt, it can quickly understand usage patterns, proper syntax and general best practices to make integration fast and painless.

4. Learn New Programming Languages: If you're venturing into a new programming language, GitHub Copilot can act as a teacher, guide and assistant simultaneously. As it generates code snippets and examples, it can help explain nuances to the language’s syntax and idiomatic patterns.

5. Generate test code: Get to validation and refinement faster by testing your code under different scenarios and test cases. Based on the logic and context of your code, it can even suggest adjustments, further reducing the manual time and effort needed in typical test-driven development practices.

You can sign up for a 30-day trial directly with Github. Then simply prompt Copilot directly from your code editor and it will begin suggesting solutions immediately. As a developer, you’re still in charge. Know what you’re looking for, cycle through options to visualize them in context, and make more informed decisions.

GITHUB COPILOT X

GitHub Copilot X represents the evolution of AI-powered coding tools, building upon the foundation laid by GitHub Copilot. While both tools share the common goal of enhancing developer productivity and streamlining the coding process, CoPilot X takes things to a new level.

Unlike the original Copilot, CoPilot X harnesses the power of an enriched and more diverse dataset, allowing it to grasp a broader spectrum of programming languages, frameworks, and libraries. So no matter the context or programming language, CoPilot X is your astute sidekick.

Context-Rich Debugging

Don’t throw the learning out with the debugging bathwater. CoPilot X goes beyond conventional coding assistance by offering insightful explanations with suggested code fixes. CoPilot X's explanations serve as valuable learning moments, helping you expand your coding prowess while remaining focused on the goals of the application.

Intelligent Documentation

CoPilot X Introduces a more sophisticated documentation process, bolstering efficiency at every step of the development lifecycle. Through automatically generated explanations, inline citations and precise answers to context-dependent questions, hurdles can be cleared faster and decisions can be made with more insight than ever before.

Embed Chat

Engineers can ask clarifying questions or auto-generate code snippets, all directly from within the IDE. Based on the context fully contained within the repository, Copilot X is armed with everything it needs to deliver clear, accurate answers, all without interrupting your workflow.

Enhanced Pull Requests

Skipping the pull-request template is how the explanations for code changes get lost in translation. CoPilot X is setting out to transform the pull request process by leveraging its advanced code analysis. When a PR is initiated, Copilot X auto-generates descriptions and a precise summary of the changes made. Communication is dramatically streamlined. Momentum keeps moving forward.

ChatGPT

ChatGPT is an advanced natural language processing conversational model that can be directly used by developers for tasks such as conversational agents, customer support, and content generation. It provides an interactive and intelligent chatbot experience.

As a senior developer, you possess a wealth of knowledge and experience in solving complex problems. ChatGPT can enhance your capabilities by providing real-time assistance and valuable insights.

Think of it as your sounding board, field guide and assistant all in one.

1. Collaborative Brainstorming:

Engage in dynamic discussions with ChatGPT to explore different approaches and solutions. Describe your project challenge or objective, and ChatGPT can generate creative ideas, alternative strategies, and potential solutions. This collaborative brainstorming process can help you uncover new perspectives and overcome roadblocks.

Example interaction:

Developer: "I'm facing a performance issue in our application. What are some possible optimizations we can consider?"

ChatGPT: "There are several optimization techniques we can explore. One approach could be to implement caching mechanisms to reduce database queries. Additionally, optimizing algorithms and leveraging asynchronous processing can also improve performance. Let's dive deeper into each option."

2. Code Assistance:

When confronted with complex code issues, ChatGPT can serve as a helpful companion. Describe the problem you're encountering, share snippets of code, and seek guidance on potential solutions or debugging techniques. ChatGPT can provide suggestions, point out possible errors, and offer best practices based on its extensive training in programming knowledge.

Example interaction:

Developer: "I'm getting a 'NullPointerException' error in this section of code. How can I troubleshoot and fix it?" ChatGPT: "NullPointerException usually occurs when you're trying to access a null reference. To debug this, you can start by examining the line mentioned in the error message. Check if any variables involved are properly initialized or if there are any missing object assignments. Additionally, you can use debugging tools to step through the code and identify the exact point where the exception is thrown."

3. Documentation and Research:

ChatGPT can serve as a valuable research assistant, helping you find relevant documentation, articles, and resources to deepen your understanding of specific topics or technologies. Simply provide the context or ask questions, and ChatGPT can suggest authoritative sources or summarize relevant information.

Example interaction:

Developer: "I need to integrate a payment gateway into our application. Can you provide me with some documentation or tutorials?"

ChatGPT: "Certainly! For integrating payment gateways, you can refer to the official documentation of popular providers like Stripe, PayPal, or Braintree. They offer comprehensive guides, SDKs, and sample code to assist you throughout the integration process. Additionally, I can fetch specific tutorials or articles if you have any particular requirements."

Chat GPT & POC Creation

ChatGPT is also invaluable tool in Proof of Concept (POC) prototyping, allowing you to gauge viability of potential solutions with remarkable speed and accuracy.

Lets explore an example scenario where ChatGPT can help generate an index.html file to calculate the distance between two points using JavaScript.

1. Describing the POC Objective:

Engage with ChatGPT and clearly outline the objective of your POC.

Example interaction:

Developer: "I'm working on a Proof of Concept (POC) and need to generate an index.html file. The objective is to calculate the distance between two points using JavaScript. Can you assist me in creating the initial code structure?"

ChatGPT: "Certainly! I can help you with that. Let's start by creating the basic structure of the index.html file with the necessary JavaScript code to calculate the distance between two points."

2. Collaborative Code Generation:

Engage in an iterative conversation with ChatGPT to generate the code for your POC. Describe the logic and steps involved in calculating the distance, and ChatGPT can provide suggestions and help you refine the code.

Example interaction:

Developer: "To calculate the distance, we need to use the Pythagorean theorem. We'll need the x and y coordinates of the two points. Let's start by creating two input fields for the coordinates and a button to trigger the calculation. How can we implement this in JavaScript?”

ChatGPT: "To achieve that, we can start by creating the HTML structure with the input fields and button. Then, we'll use JavaScript to fetch the values entered by the user, calculate the distance using the Pythagorean theorem, and display the result. Here's an example code snippet to get you started..."

Example using Chat GPT to generate a javascript code for distance calculation

3. Iterative Refinement and Testing:

Continue the conversation with ChatGPT to address any additional questions or modifications to the generated code. Refine the code based on your specific requirements and proceed with testing the POC to ensure it meets the intended objectives.

Whether it's generating code structures or refining implementation details, ChatGPT serves as a valuable companion in the iterative process of POC development, enabling you to quickly test ideas and assess their potential before investing significant resources.

Advanced use

API or Framework integrations can bring AI-enabled technology even deeper into your solution. While these integrations present exceptional upside and exciting possibilities, it’s critical to closely evaluate whether your particular solution requires it in order to avoid unnecessary complexity and potentially time-consuming tasks. These are a few of the AI integrations we think are most promising:

API Integration

1. OpenAI API (ChatGPT API): OpenAI provides an API that allows developers to integrate ChatGPT into their applications. By using the API, developers can send requests to the ChatGPT model and receive responses, enabling seamless integration of AI-powered chatbot capabilities.
2. Microsoft Cognitive Services: Microsoft Cognitive Services offers a suite of APIs for computer vision, speech recognition, natural language understanding, and decision-making. Developers can integrate these APIs into their applications to add AI capabilities in a modular manner.
3. Amazon Web Services (AWS) AI Services: AWS provides a range of AI services with APIs that developers can integrate into their applications. Services like Amazon Rekognition, Amazon Polly, and Amazon Comprehend offer pre-trained models accessible through APIs for image analysis, text-to-speech conversion, and natural language processing, respectively.
4. Google Cloud AI: Google Cloud AI offers various APIs that developers can utilize for different AI tasks. APIs like Google Cloud Vision, Google Cloud Speech-to-Text, and Google Cloud Translation allow the integration of AI capabilities related to image recognition, speech recognition, and language translation.
5. Microsoft Azure AI: Microsoft Azure AI provides APIs for computer vision, speech recognition, natural language processing, and more. Developers can integrate Azure Cognitive Services APIs and Azure Machine Learning into their applications for adding AI functionalities.

Frameworks

1. TensorFlow: TensorFlow is an open-source machine learning framework that provides tools and libraries for developers. It allows direct usage for building and deploying machine learning models in tasks like image recognition, natural language processing, and deep learning.
2. PyTorch: PyTorch is a popular open-source machine learning library that offers a flexible and dynamic approach to building neural networks. Developers can directly use PyTorch for research, prototyping, and production deployments.
3. Keras: Keras is a high-level neural networks API written in Python that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It offers a user-friendly interface for developers to directly build and train neural networks.