Generative AI, or Gen AI, is a transformative technology that is changing the way industries use to create content, including audio, code, text, images, simulations, and videos. According to McKinsey, the Gen AI applications add upto $4.4 trillion to the global economy annually. Hence, it allows us, the experts, to jump to the conclusion that within the coming three years, anything in the technology space that is not connected to AI will become obsolete or ineffective.
When ChatGPT came into the picture in November 2022, everyone reacted to it and Gen AI with fear. However, it is essential to notice that these tools are just assistants. Imagine an assistant providing you with ideas on how to begin your code or how to finish the code.
Gen AI solutions make the work of many coders and software developers of all skills easier, as they automatically produce code or suggest code based on a user prompt specifying the specific functionality.
AI-generated code might not hit the bull’s eye every time, but it gives a starting point for developers to start coding. It makes it faster and easier to code as the coders do not have to spend their time manually typing out every line of the code. That’s the benefit of Generative AI!
Besides this, there are more benefits as well. Let’s see in this blog how Gen AI tools can improve and boost productivity of developers.
How Developers Can Use Generative AI Applications?
Software developers frequently use ChatGPT and GitHub’s Copilot to generate or complete code for a particular feature. While ChatGPT provides a simple text-based interface for asking queries regarding a wide range of topics (since it is trained on data available public all over the Internet), Copilot is more aligned towards fitting directly within the developer’s integrated development environment. Being trained on all the publicly accessible code that lives on GitHub, Copilot offers advanced functionalities, like an autocomplete feature where it suggests the next line of code.
However, Gen AI technology has much more potential than answering developers’ queries and helping them complete the code. For instance, it can be used to write simple atomic codes.
Generative AI can recommend enhancements to existing code by discovering redundant or inefficient sections. It can even automatically generate test cases, produce test inputs, and analyze code, which helps increase test coverage and detect potential issues at the beginning of the development cycle.
Generative AI can help software development teams find and automatically fix bugs by analyzing large codebases. This may result in faster development cycles and more reliable software overall.
It can be used to complete tasks that are time-consuming when done manually, but it can be checked quickly for accuracy and completeness, like changing the data format or code from one form to another. Also, it can be used to generate a text summary of the code, but expertise will be required to understand the technical jargon and whether the summary actually resonates with the code.
Gen AI applications can be used to complete tedious tasks, like setting up boilerplates, correctly structuring API requests, and refactoring. These tools can even help the developers to find a solution to open loopholes. For instance, issues related to API and framework that are new to the developer and these tools can help to find an approach to close the loop.
Things to Keep in Mind:
There are three things that developers should keep in mind before relying on Generative AI technology:
- It is never wise to copy-paste the code suggested by the Gen AI tools as it is. Some modifications will be required, and some lines will need to be tweaked because the LLM doesn’t have the full context of the projects and their objectives.
- Do not give Gen AI tools too much responsibility. Asking a complex or open-ended query will lead to hallucinated answers. Hence, it is best to ask problems that have predictable patterns.
- Avoid putting sensitive or business-critical information in the input prompt, as it may be used to train the model.
The best way to get the right output is to provide more context to the Gen AI. Clearly mention your requirements and the approach you need LLM to follow to generate the output. The more context, the better the output. You can even use prompt engineering techniques to make the most out of your queries.
Prompt Engineering Tips: How to Get the Best Output From Generative AI Applications
The outputs from Gen AI tools depend upon two things: the training dataset and the input prompt. Writing clear and effective prompts is so critical that it gave rise to prompt engineering, which refers to creating precise and effective prompts to get accurate AI-driven content from Gen AI technology.
Industry professionals are adding prompt engineering as a time-saving skill in their portfolios because this is the demand of the modern world. Besides, one doesn’t even need to be a skilled programmer to become a prompt engineer; all you need is good language skills, analytical thinking, expertise in natural language processing and LLM capabilities.
Prompt engineers apply top approaches to extract the desired and accurate code from the Generative AI applications. Here are some prompt engineering techniques that you can use to boost your productivity:
Zero-shot prompting means providing large language models with no prior context or example. This is best and frequently used when users require answers to simple queries on general topics.
One-shot prompting involves providing one example or piece of context before asking your main query. This helps the Gen AI tool understand what needs to be done.
Informational Retrieval prompting is just like treating the Generative AI application as a search engine. It usually entails asking a specific question to obtain detailed and informative output. The validity and accuracy of information depend upon the LLM’s data sources, as Gemini can offer better results since it has access to current Internet information. Still, ChatGPT curates output based on what happened before January 2022.
- Chain-of-Thought Prompting
Instead of providing a single but long and complex prompt, you can begin with a single and simple prompt and ask the Gen AI tool to elaborate more on the answers. You can even ask the tool to explain its thought process to understand how it reached the specific conclusion.
Negative prompting is all about telling the Generative AI applications to exclude all the possible, which you don’t want in your answers. It helps to eliminate all the undesired possibilities, and you get an output that aligns with your expectations.
Most times, when you use Gen AI tools, like ChatGPT, they tend to give outputs full of fluff. Hence, it is best to remind the Gen AI applications to be brief, or you can ask them to summarize the output so you can get your desired answer with less fluff.
Top Generative AI Applications for Software Developers
Here is the list of some best AI platforms with their use cases:
1. Typo
Typo automatically analyzes your code as well as pull requests to identify issues and recommends auto-fixes before merging.
Use Case
Code review is a time-consuming process. Typo indicates alerts inside a git account and helps developers find issues as soon as a PR is raised. It provides you with a thorough overview of performance, security, and vulnerability issues. It recommends auto-fixes along with best practices to move things together more quickly and efficiently.
2. Github Copilot
Github Copilot is an AI pair programmer that suggests style options for your code automatically.
Use Case
Your software development project will require coding at some point. On the other hand, it requires a lot of work to do manually. Github Copilot allows you to test and choose what actions to take with your code by putting suggestions from your linked or active code files. Additionally, it guarantees that unsafe coding patterns are excluded and prevents harmful public code suggestions.
3. Tabnine
With the use of deep learning, Tabnine is an AI-powered code completion tool that makes code suggestions while you type.
Use Case
Writing code can make it difficult to focus on other important tasks. Over time, Tabnine can tailor code to your coding habits and make accurate suggestions. Additionally, it incorporates well-known IDEs with programming languages like Python and Javascript for quick setup and less context switching.
How to Eliminate Risks Associated with Generative AI?
Generative AI has many flaws and errors despite its increasing effectiveness due to algorithm fine-tuning. They are not always correct; therefore, human review is required after assigning certain tasks to AI tools. Here are several ways to lower the risks associated with Generative AI applications:
1. Implement quality control practices.
Create rules and regulations to handle ethical issues like accuracy, privacy, fairness, and transparency in software development projects. Make sure to track on a system that measures potential biases, performance metrics, and model accuracy.
2. Provide Generative AI Training.
Provide generative AI training and mentorship. This will reduce the risk and raise departmental AI literacy. Assist them in becoming aware of these tools’ limitations and how to use them effectively.
3. Understand AI as an Assistant, not a Replacement.
Make sure your developers understand that the purpose of these generative tools is only to be an assistance. To fully utilize AI’s capabilities, encourage cooperation between these tools and human operators.
The Bottom Line,
Generative AI is set to completely transform the software development by increasing developer productivity, automating tasks, and facilitating the development of more creative and effective software applications. As artificial intelligence (AI) technologies progress, developers and organizations must adjust to these developments and utilize generative AI technology to maintain competitiveness and produce software solutions of superior quality.
