As managers, we are always looking for ways to streamline our team’s processes so that everyone has more time to focus on their core responsibilities. While we already know that artificial intelligence has helped technology companies and developer teams on many fronts, the important thing now is to have the wisdom to choose, train, and extract what we can from it at this stage of the digital revolution. This is where both AI coding assistants and the field of prompt engineering come into play.
According to Gartner, 63% of companies are testing or implementing AI coding assistants, and the forecast is that by 2028, three in four software engineers will adopt such a solution. These tools represent an extra capability that goes beyond code generation and completion. It works as a collaborative assistant, which not only improves developers’ efficiency but also optimizes the entire process.
Meanwhile, improvements in prompt engineering—crafting instructions for an AI model to generate an adequate response—have helped AI understand and perform industry-specific work across different sectors and companies.
Real-world Examples of GenAI Impact
Software development teams frequently encounter several challenges that can significantly impact their productivity and the quality of their output. As mentioned, one of the primary issues is time constraints, often driven by tight deadlines and market pressures, which usually lead to a rushed development process. The complexity of projects and how to maintain high code quality are other significant hurdles in the operation.
Generative AI is transforming the way devs work by significantly boosting productivity. They can now integrate AI-powered tools into their development environments and continuously update their models with relevant data to accelerate their coding speed. That’s how they can improve productivity by:
- Automating repetitive coding
- Enhancing coding efficiency
- Improving code quality
- Streamlining code reviews
Moreover, AI-assisted coding offers faster debugging and learning opportunities as it often provides explanations and insights, helping developers learn new coding concepts and techniques and language and framework support, accommodating diverse developer preferences. When AI code assistants are activated in the software development cycle, they free the programmer to do so much more in their programming workflow.
Meanwhile, prompt engineering has been gaining traction, as it enables teams to effectively communicate their industry-specific needs to GenAI-powered tools.
AI Coding Assistants: A Comparative View
The software development industry is constantly evolving, with new tools emerging to help developers write code more efficiently. Here are five standout AI coding assistants that have gained significant traction in the market:
GitHub Copilot
This is one of the most notable tools in the field of coding assistants. A collaboration of GitHub and OpenAI, this tool is highly integrated into the development environment and offers code suggestions, comments, and automatic documentation, among other features. It is powered by state-of-the-art language models, and it is known for its ability to support multiple programming languages
Pricing: paid.
Cody (Sourcegraph)
With Cody, software engineers get an AI assistant that uses intelligent code context to provide answers to technical questions, generate code blocks, provide test cases, and more.
What separates it from other AI assistants on the market is that the chatbot is aware of more than just the code when it comes to the file your developer is looking at. If permitted, Cody can search and navigate a company’s entire codebase using Sourcegraph’s specialized technology.
Pricing: free and paid plans.
Amazon Q Developer
Amazon Q Developer is your AI coding assistant when it comes to building on AWS, answering your questions about software development, generating code, updating your code language version, and explaining, refactoring, or optimizing snippets. It supports languages such as JSON & YAML (AWS CloudFormation), HCL (Terraform), and CDK (TypeScript, Python), to name a few.
Pricing: free.
Tabnine
This AI assistant is known for its efficiency and speed in generating code suggestions. It uses machine learning techniques to predict what you want in your code and provides real-time contextual suggestions. Moreover, it is compatible with a wide variety of programming languages and IDEs.
Pricing: paid.
Tabby
Our last suggestion offers a free and powerful alternative to GitHub Co-Pilot by providing an open-source, self-hosted AI coding assistant. With a variety of features and an active community of devs, it has become a popular choice among teams looking for more control and freedom in their development workflow.
Pricing: free.
Keep in mind that it’s important to choose the ideal AI coding assistant depending on the specific needs of the developer and the platform they are working on. Each of these tools has unique features that may be better suited to different development contexts.
Mastering Prompt Engineering
Think about how you would teach your pet a new trick: you would provide clear, direct instructions to guide them. Similarly, prompts serve as detailed instructions that help AI models understand what is expected of them, ensuring their responses are accurate and relevant.
While it may seem simple to use prompts on ChatGPT, the real complexity lies in making AI understand context and nuances like humans do. This requires a deep understanding of machine learning principles and human language constructs.
Crafting prompts has become a science in itself, as their quality directly impacts the precision of responses generated by AI models.
Check out this GitHub guide to prompt engineering and LLMs and share it with your team.
The Human Factor: Skilled Professionals as Key Drivers
Although generative AI tools can now assist developers with coding and many of the development tasks, they are not infallible and need guidance from a human. There needs to be a maestro who will conduct the orchestra and come up with complex arrangements that address ethics, accountability, and security.
Recent advancements in artificial intelligence have made the role of a software engineer even more complex, but still fascinating. Recently, over seven new AI-related roles have emerged, making the role of the software engineer even more complex yet still interesting.
It’s All About the Strategy
The synergy between human prowess and AI capabilities is opening up exciting possibilities. It’s time to experiment, learn, and discover how it can transform your workflow. Remember, the most successful teams are those who adapt to and leverage new technologies.
With a diverse pool of top-tier remote rock stars, we can connect you with professionals who are versed in cutting-edge technologies, be it software development or artificial intelligence. Reach out to us for assistance with expanding your team through our staffing services.
