Whether developers need to take philosophy classes or not, the reasoning is sound. Generative AI transformed the way we think and work. Unlike in the past, when developers took instructions from a team lead and executed tasks as individual contributors, now they’re outsourcing problem-solving and code generation to AI tools and models.
Understanding the Problem First
In the new world of GenAI, well-rounded developers must fully understand the problem and required outcome before GenAI-assisted problem-solving begins. Their understanding of the problem space must match that of a product manager or end-user. After all, the wrong prompt could result in a response that perpetuates the problem. Give an image generation tool like Dall-E a basic prompt (show me a developer in an office), and follow up with a detailed prompt (show me a developer in an office coding on a laptop in an urban environment with young co-workers). You’ll end up with two completely different pictures.
Key Soft Skills for Developers
What soft skills matter most in the age of AI? Four that stand out are reasoning, curiosity, creativity, and accountability.
Reasoning and Context Matter
One of the most important lessons I learned from a previous boss is that context matters. Suppose you’re trying to convince someone to do something; explaining “the why” is the most important part. It’s what creates linkage and trust. GenAI doesn’t do that on its own. We’re at a point now where GenAI produces a good but not great output. A human touch is still needed to inject that last 20% of work to push the chatbot and iterate. You have to treat your GenAI like an intern — someone who needs coaching and context so that they can ultimately help you get what you need and learn more about the process along the way. That means your job is to provide the reason and context to convince the AI/intern to do things correctly.
Embrace Curiosity and Exploration
When they use GenAI, developers have to probe for more information continually. They should think of themselves as reporters uncovering facts. Is there anything else I missed? After AI creates a first take, probe further in a second version, making the questions more action-oriented. Think of it as having a conversation with the GPT. If you’re creating content, tell the GenAI to pretend it’s an employee, share three questions an employee would have, and then answer them. Then, have the GPT rework the draft with the answers again. Using this approach while embracing the diversity of thought with your unique skill set and problem-solving abilities will be essential to effectively serve a diverse set of customers.
Creativity in Developer Prompts
GenAI does what it’s told. It culls information from available sources and applies it systematically based on the prompts that it is given. The creativity a developer exercises in delivering those prompts can encourage an AI tool to present coding options that the organization may not have anticipated. Like writers who keep their works fresh by varying their syntax, pacing, and tone, developers can issue directives in different ways to elicit “out-of-the-box” responses.
Accountability in the Age of AI
We’re on the border of an ethical conundrum, and well-rounded developers will be needed to get us through. Just because developers can get GenAI to do something doesn’t mean they should. Developers are now co-creating IP. Who owns the IP? Does the prompt engineer? Does the GenAI tool? If developers write code with a certain tool, do they own that code? In an industry where tool sets are moving so quickly, it varies based on what tool you’re using, what version of the tool, and what different tools within certain vendors even have different rules. Intellectual property rights are evolving. It’s like the wild, wild west. Reasoning through that and understanding the context of what developers should get their tools to do is an important skill.
Conclusion
For top-performing developers, the increasing integration of GenAI into development workflows does not diminish the importance of hard skills. However, for developers who seek to advance their careers and contributions, up-leveling their soft skills like customer empathy and critical thinking will go a long way in making them well-rounded developers in a post-GenAI landscape.
The advancement of developers’ soft skills will not only make them more effective collaborators in the workplace. Still, it will also reinforce their value to organizations exploring leveraging GenAI to achieve new levels of productivity and success.
Whether developers need to take philosophy classes or not, the reasoning is sound. Generative AI transformed the way we think and work. Unlike in the past, when developers took instructions from a team lead and executed tasks as individual contributors, now they’re outsourcing problem-solving and code generation to AI tools and models.
Understanding the Problem First
In the new world of GenAI, well-rounded developers must fully understand the problem and required outcome before GenAI-assisted problem-solving begins. Their understanding of the problem space must match that of a product manager or end-user. After all, the wrong prompt could result in a response that perpetuates the problem. Give an image generation tool like Dall-E a basic prompt (show me a developer in an office), and follow up with a detailed prompt (show me a developer in an office coding on a laptop in an urban environment with young co-workers). You’ll end up with two completely different pictures.
Key Soft Skills for Developers
What soft skills matter most in the age of AI? Four that stand out are reasoning, curiosity, creativity, and accountability.
Reasoning and Context Matter
One of the most important lessons I learned from a previous boss is that context matters. Suppose you’re trying to convince someone to do something; explaining “the why” is the most important part. It’s what creates linkage and trust. GenAI doesn’t do that on its own. We’re at a point now where GenAI produces a good but not great output. A human touch is still needed to inject that last 20% of work to push the chatbot and iterate. You have to treat your GenAI like an intern — someone who needs coaching and context so that they can ultimately help you get what you need and learn more about the process along the way. That means your job is to provide the reason and context to convince the AI/intern to do things correctly.
Embrace Curiosity and Exploration
When they use GenAI, developers have to probe for more information continually. They should think of themselves as reporters uncovering facts. Is there anything else I missed? After AI creates a first take, probe further in a second version, making the questions more action-oriented. Think of it as having a conversation with the GPT. If you’re creating content, tell the GenAI to pretend it’s an employee, share three questions an employee would have, and then answer them. Then, have the GPT rework the draft with the answers again. Using this approach while embracing the diversity of thought with your unique skill set and problem-solving abilities will be essential to effectively serve a diverse set of customers.
Creativity in Developer Prompts
GenAI does what it’s told. It culls information from available sources and applies it systematically based on the prompts that it is given. The creativity a developer exercises in delivering those prompts can encourage an AI tool to present coding options that the organization may not have anticipated. Like writers who keep their works fresh by varying their syntax, pacing, and tone, developers can issue directives in different ways to elicit “out-of-the-box” responses.
Accountability in the Age of AI
We’re on the border of an ethical conundrum, and well-rounded developers will be needed to get us through. Just because developers can get GenAI to do something doesn’t mean they should. Developers are now co-creating IP. Who owns the IP? Does the prompt engineer? Does the GenAI tool? If developers write code with a certain tool, do they own that code? In an industry where tool sets are moving so quickly, it varies based on what tool you’re using, what version of the tool, and what different tools within certain vendors even have different rules. Intellectual property rights are evolving. It’s like the wild, wild west. Reasoning through that and understanding the context of what developers should get their tools to do is an important skill.
Conclusion
For top-performing developers, the increasing integration of GenAI into development workflows does not diminish the importance of hard skills. However, for developers who seek to advance their careers and contributions, up-leveling their soft skills like customer empathy and critical thinking will go a long way in making them well-rounded developers in a post-GenAI landscape.
The advancement of developers’ soft skills will not only make them more effective collaborators in the workplace. Still, it will also reinforce their value to organizations exploring leveraging GenAI to achieve new levels of productivity and success.
Author: Muhammad Talha Waseem
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