Boost Developer Productivity with Generative AI: Tips from Top Engineers

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Generative artificial intelligence (Gen AI) revolutionizes how software developers write code. Introduced just a few years ago, this emerging technology has quickly become widespread. According to the 2023 State of DevOps Report, over 60% of respondents use AI regularly to analyze data, generate and optimize code, and learn new skills and technologies. Developers constantly find new applications and improve their methods with these rapidly evolving tools.

How are software developers adapting to this new era where software can write software? What will the role of a software engineer look like as this technology surpasses human capabilities in code writing? Will there always be a need for a real human expert to guide the process?

Our experts discuss what Gen AI excels at, its limitations, how to leverage generative AI for software development, and the software industry’s future if these trends continue.

Generative AI: Developers’ New Best Friend

When it comes to AI tools for software development, OpenAI’s ChatGPT and GitHub Copilot are the most popular. ChatGPT offers a simple text-based interface where users can ask the large language model (LLM) about virtually any topic. It’s trained on publicly available internet data.

On the other hand, Copilot integrates directly into a developer’s integrated development environment (IDE), providing advanced autocomplete functionality by suggesting the next line of code to write. It’s trained on all publicly accessible code on GitHub. Together, these tools theoretically offer solutions to almost any technical problem a developer might encounter.

The real challenge is knowing how to use these tools effectively. Developers must understand which tasks best suit AI and how to craft their prompts to get the desired results.

Boosting Productivity Through Generative AI Tools

Gen AI can significantly enhance developer productivity in coding, learning, and research tasks—but only when used correctly. Without enough context, ChatGPT might generate plausible-sounding but incorrect responses. Incorrect context can be even worse: if ChatGPT is given a poor solution, as an example, it will base future responses on that faulty information.

Experts recommend strategies like assigning clear roles to Gen AI and providing relevant technical information to maximize its effectiveness. The more context you provide, the better the results—just be cautious not to share sensitive or private data with public models like ChatGPT, as this information could be used to train the models.

Master the Art of Prompting for Ideal AI Responses

Your prompt Gen AI tools can significantly affect the quality of the responses you get. The importance of prompting has led to a new subdiscipline known as prompt engineering. This involves writing and refining prompts to produce high-quality outputs. AI generates better responses when given clear context, a defined scope, and a description of the desired response, such as “Provide a numbered list in order of priority.”

Techniques to draw out the best responses from LLMs:

Few Shots, Zero Shots, and One-Shot Learning

Prompt engineering specialists use various techniques to extract the best responses from LLMs. One key approach is to provide minimal necessary context, relying mainly on the model’s prior knowledge and reasoning capabilities. This can involve giving no examples, one example, or just a few to ensure the AI focuses on its built-in understanding and logic.

Iterative Prompting

Prompt engineering specialists guide the AI to the desired outcome by refining its output through iterative prompts. This can involve asking the AI to rephrase or elaborate on previous responses. Users can progressively shape the AI’s answers by tweaking and adjusting the prompts to meet their needs.

Negative Prompting

Experts also suggest the AI by specifying what not to do. This includes instructing the AI to avoid certain types of content or responses. By clearly outlining these restrictions, users can help ensure the AI’s output remains relevant and appropriate to the task.

Train-of-thought Prompting  

Ask the AI to explain its thought process in steps. This will help users understand how the AI arrives at its answer, provide insight into its reasoning, and ensure the output is logical and well-founded.

How Generative AI Will Revolutionize Software Development?

Some claim that traditional programming skills will soon become obsolete with the rise of Gen AI tools. AI will eventually be capable of building entire apps from scratch, making coding expertise unnecessary.

Currently, one of the most advanced tools in AI-generated UIs can create React code using UI components. While this is useful for early prototyping, it still falls short of replacing skilled UI developers who can implement custom brand guidelines. The technology needs significant advancement before it can compete with human expertise.

Forward-thinking organizations should encourage their teams to upskill with these new tools to boost developer productivity and educate all stakeholders about the security risks of incorporating AI into daily workflows. Ultimately, the technology is only as powerful as those who use it.

Getting Started

PureLogics can help you leverage generative AI to accelerate development while upholding trust, security, and compliance principles. Our expert Gen AI developers can expedite application modernization and create automation to scale IT environments quickly. Give us a call today!