Generative AI has the potential to lead developers to great things, but data will remain the major differentiator
Tags: developer, generativeai, data, twilio
Published: 2023-08-23 16:00:00

There’s no doubt that generative AI is transforming the role of the developer. According to a recent [GitHub survey](https://github.blog/2023-06-13-survey-reveals-ais-impact-on-the-developer-experience/), 92% of developers are currently using AI coding tools – either as a way to generate code quickly and more efficiently or to incorporate AI into their products (such as a chatbot that uses natural language processing or a recommendation engine that improves the user experience).

While there are some fears about AI replacing human workers, producing sloppy code or leading to a loss of intellectual property or personally identifiable information (PII), the response from the developer community as a whole has been extremely positive. The GitHub survey shows that 70% of developers feel that AI coding tools will offer them an advantage at work.

Can generative AI really be considered a differentiator if everyone is already using it to some degree? How can developers use generative AI tools to give them and their companies a leg up on the competition?

The depth, resiliency and quality of the data that is fed into these generative AI engines may be the answer.

##The possibilities of generative AI for developers
Developers are using AI coding tools to identify bugs, translate code into other languages and make code more efficient. These tools can produce thousands of options at scale, allowing a developer to sort through the alternatives and use human reasoning to choose the best solution. This improves code quality and streamlines workflows – ultimately speeding time to market. 

On the other end of the spectrum, developers are incorporating generative AI directly into their company’s products. Chatbots are a great example. Using natural language processing (NLP), AI-powered chatbots can learn customer sentiment and intent over time and use those insights to provide better (and faster!) service in the future. Providing the appropriate answer or service to help desk questions puts service delivery in the right context – ultimately improving the customer experience and loyalty while reducing service costs.

Used in the right way, generative AI creates efficiencies and economies of scale that allow developers to focus on the big picture and experiment with new ideas and functionalities — perhaps leading to a golden age of innovation.

##The catch? It’s all about the data
Generative AI is only as good as the data you feed it. Large language models (LLMs), which power the most popular generative AI engines today, are capable of taking information and producing unlimited amounts of coherent text — such as code — at scale. From there, developers can pick the best solution, clean it up, add the human touch and move onto the next thing. But output is directly correlated to input. Feeding generative AI engines with clean, relevant, bias-free data is critical and can be a differentiator in the marketplace.

Take the chatbot example above. Using a public-facing generative AI tool such as ChatGPT tends to produce generic answers to service questions. However, proprietary models trained with company-, industry- and product-specific information will generate personalized responses using relevant and familiar terminology. It’s the quality and depth of the data fed into the generative AI engine that puts service inquiries into the right customer context and leads to positive outcomes. Data is the differentiator.

Here are three things developers need to keep in mind when ensuring the code they are using to model generative AI engines is clean, relevant and bias-free:

1. **Know how data is collected**
Any political junkie knows that the source of information is absolutely critical to finding the truth, and developers should take notice. You need to know where the data you feed into your generative AI engines is coming from. How was it collected? By whom? When was it collected? This ensures that the data is relevant and isn’t biased. Following these data collection principles ensures the data you’re collecting will lead to better outcomes.

2. **Take steps to ensure your data is unbiased**
Data noise can change outcomes, especially when you’re dealing with narrow objectives. That said, data needs to be put into the right context given the problem you’re trying to solve. One way to do this is to make sure you have a diverse group of people in the room when training your models. Different perspectives make it more likely that you will have a well-rounded dataset that is clean and free of bias. This puts data in the right context and ensures you are getting the unbiased outcomes you expect.

3. **Ensure security and privacy**
Whether you’re using your own generative AI engine or a public tool like ChatGPT, it’s critical that you secure your data and safeguard potential PII. Make sure you take out or redact sensitive information and get consent from all parties before you feed their data into your models.

## The future of generative AI
It’s an exciting time to be a developer in the age of AI. Generative AI can enhance the productivity of developers and improve the overall experience. However, the level of innovation you reach will come down to the quality of the data you feed into your generative AI engines. It’s important that you know how, where and when your data is collected, that you use data in the right context to avoid bias, and that you apply the appropriate security and privacy controls to protect intellectual property and PII. Generative AI is giving us an opportunity to do some really great things, but only if the developer community takes a few critical steps to ensure the right outcomes.

Today, Twilio is having its annual customer & developer conference, [SIGNAL](https://signal.twilio.com/). This year I was able to give a live demonstration on the power of generative AI and the difference that data can have when it comes to AI. You can watch my demo [here](https://signal.twilio.com/2023/sign-in?rId=3759160). 

_Kaelyn Chresfield is a developer evangelist at Twilio, a company that enables businesses to use communications and data to add intelligence and security to every step of the customer journey, trusted by more than 10 million developers across the globe._