A step-by-step plan for safe use of GenAI models for software development

If you are a large-scale company, the recent AI boom hasn’t escaped your notice. Today AI is assisting in a large array of development-related and digital-related tasks, from content generation to automation and analysis.

GenAI software development

The development of AI is rapid and with it being a largely unexplored field, ethic, economic, social, legal concerns have surfaced. Amidst heated discussions about the intellectual property of AI-enhanced content and whether AI will be capable of fully substituting human labor (and causing massive layoffs) any time soon, businesses want to capitalize on the advantages the use of generative AI (GenAI) offers, but safely.

In this article we delve into the topic of building an environment for the use of ChatGPT and similar GenAIs in software development that is both safe and beneficial for all involved.

Addressing the risks and busting the myths

The relative novelty and explosive proliferation of AI has created a great number of misconceptions and informational chaos. Let’s look at some common risks and circulating myths associated with ChatGPT and see how they can be countered or mitigated.

1. AI is developing faster than governmental regulations for it

We have seen this before with blockchain and cryptocurrency. Processes of AI development are rapid, while legal procedures and regulatory processes lag behind. This creates a large gray zone which is hard to navigate: plenty of blank spots, few precedents. This can potentially lead to businesses practicing unsafe or unethical methods of working with ChatGPT or even something that can be deemed illegal in the future.

To counter this problem, you can create a clear code of conduct pertaining to the use of ChatGPT in your organization and make it mandatory for employees to familiarize themselves with it.

This code should cover as many aspects of working with GenAI, as possible. For instance, it should include the types of tasks for which the use of ChatGPT is allowed in your organization, and the types of access required. Since this field has a lot of blanks, it is best to work with trusted experts or outsource the creating and implementing of GenAI practices to someone with expertise in the field.

2. Debates about intellectual property, oversharing and privacy risks

There are plenty of known cases when people tried to train AI with sensitive or private data or used it as inputfor analysis. There’s still much debate about whether this data becomes available to someone outside your organization. Another heated debate is how to attribute intellectual property rights for any product where ChatGPT was used. Keep in mind that if an employee uses their personal account for working with ChatGPT, the outcome is their intellectual property, not your company’s.

This is why it is important to introduce and observe in your AI-related policy rules for working with sensitive, private or copyright-protected information.

3. Unpredictability of AI development

This is another factor of risk and much speculation. AI is still largely a tabula rasa, and although it is unlikely that technological singularity will become a reality anytime soon, it is still important to remember that the future of AI is highly unpredictable. Because of that, it is hard to know whether we will be able to exert as much control over AI and its development in the future. Another thing that is hard to predict is how dependent on AI our work can become, which is also a big factor of risk.

We can mitigate this risk by creating and implementing workflows for working with ChatGPT that are flexible and agile. Such workflows should scale easily and have a great capacity of adapting to changes, both in development and in regulations.

4. AI will eventually substitute human labor

This is still largely a conspiracy theory domain, but even now we see how much work can be automated with the help of AI. This myth is easily busted by anyone who tries to use AI for more complex tasks. Today, the quality of AI output is very dependent on the quality and precision of the input, but to write coherent, useful prompts that generate accurate and usable results, an operator usually needs to have at least some basic training of working with AI, and domain knowledge is always required.

The correlation between the experience in prompt engineering and domain knowledge on the one hand and AI output quality on the other hand is direct. This means that to effectively use AI, efficient and qualified staff is needed.

One way to go around this ethically sensitive subject is to drive the ChatGPT-related work more towards automation and enhancing human-done work by adding precision, coverage, taking up mundane and repetitive tasks and eliminating errors that happen due to the human factor.

5. Complexity of AI-related processes and documentation

With AI being still uncharted territory, most businesses still have to go the way of trial and error before they manage to establish clear and working AI workflows, document protocols for ChatGPT-oriented tasks. The lack of accumulated knowledge makes businesses navigate blindly. This can lead to costly errors, loss of efficiency and waste of time and resources.

The best solution for this problem is to delegate the implementation of AI-processes and documentation to experts, hiring reliable in-house staff or outsourcing these tasks to professionals.

Best practices and steps for building a safe environment for the use of GenAI in software development

Implementing processes for working with AI should be done carefully and with keeping in mind that such solutions need to be tailored and consider many business and domain-related specifics and context. Here are some best practices that will help you navigate these murky waters.

1. Focus on user consent, data privacy, security and compliance

Learn which regulations are already in place for AI in your region and monitor governmental processes closely to be ready for the coming legislative acts. If your product features AI-based services, ensure that users are fully aware of the fact, know which data is collected and give their consent before working with your product. For development processes, avoid using sensitive, private or copyrighted data to train AI or to generate outputs.

2. Human-in-the-loop practices

Human-in-the-loop are a set of design and operational practices where human judgment is integrated into AI-driven systems and processes. This approach limits the potential autonomy of any AI and gives you more control and oversight, especially at critical decision-making points. The concept is essential in scenarios where AI’s decisions may have outstanding ethical, legal, or personal implications.

3. Responsible disclosure and deployment

If you see that your AI-related work has steered you into the wrong direction, you should always be prepared to drop it. Potentially harmful ChatGPT results and trained AI models that are determined to pose a risk should be discarded and shut down, possibly without having wrought too much havoc in your other processes.

4. Transparent documentation

Clear and transparent protocols of working with AI, uniform procedures, absence of any ambiguity and double meanings in terms and key points are vital if you want everyone in your organization to be on the same page about where you stand with the use of AI. It also helps to quickly onboard employees and introduce AI practices at a scale.

5. Define and limit the scope of AI involvement

The exact scope of AI involvement should be defined from the start of your project. This should be put down into the necessary documentation about working with ChatGPT, which you need to make available to all employees. Tasks for which it is permissible to use AI should be specified clearly, as well as tasks where the use of AI is prohibited.

6. Engage with the AI community, raise AI awareness and literacy

As we have mentioned above, the lack of accumulated knowledge base is one of the hindering factors for ChatGPT-related work in software development. Contributing to the AI community, exchanging ideas, sharing of experience can be profitable for both your business and the community itself. It has also become a necessity to educate your staff on AI developments and to raise awareness about various issues associated with it.

7. Transparency and accountability

Although this is a more generic best practice which benefits any software development process, it is especially important for AI-enhanced tasks, a field which is still a gray zone. You should clearly determine who is responsible for what and every employee involved in working with ChatGPT should know their personal responsibility and who is the person in charge of various specific tasks or issues.

8. Continuous monitoring

As is with any rapidly developing sphere, continuous feedback and its incorporation cycle, monitoring of ChatGPT-generated outputs, review of AI-related workflows, regulations and developments in the field helps you to refine your processes and make them as efficient as possible. It will also help you to avoid many pitfalls associated with ChatGPT and other GenAIs.


Navigating the complex and rapidly evolving landscape of generative AI presents both unprecedented opportunities and significant challenges for any digital business.

By addressing the ethical, legal, and practical concerns associated with ChatGPT and similar technologies, and implementing a framework of best practices enterprise-level companies can harness the power of GenAI safely and effectively. This approach can not just help establish a failsafe environment but also ensures that AI-driven innovations enhance rather than replace human expertise, leading to more robust, efficient, and ethically responsible software development processes.

Don't miss