Ask a large language model (LLM), “Why is the sky blue?” and you’ll get a clear, scientific explanation. Ask again, but say, “Explain it like I’m five” and the response becomes more playful and accessible. Now try “Explain it like I have a PhD” and you’ll get a highly technical, detailed answer. 

Each response is correct, but the output depends entirely on the context provided in the prompt. 

This simple exercise reveals a key limitation of today’s co-pilot paradigm: the system is only as effective as the person prompting it. And not everyone knows the ‘magic words’ that unlock the best performance from an LLM. 

As the software industry increasingly embraces generative AI (Gen AI), many engineers are shifting from traditional development to ‘prompt engineering,’ which crafts detailed instructions to guide AI tools in development. Skilled prompt writers (engineers) can produce impressive results, automating everything from code generation to documentation. 

But there’s a catch: scaling this model across a team or enterprise is difficult. Most users aren’t experts in prompt design. They don’t always know the correct language, syntax, or context to get the best output. Co-pilot tools, by design, are reactive; they wait for input and can’t learn or adapt autonomously. This can result in inconsistent outcomes and underutilized potential. 

A new paradigm: Gen AI development 

Now, imagine a different approach. What if LLMs didn’t rely on constant human prompting, but instead connected into a broader system that could plan, reason, and act iteratively to achieve a specific goal? 

This is the vision behind Gen AI-assisted software development. It is a world where humans set the direction and AI agents do a lot of the actual development work – writing the code, running tests, etc. – with humans managing them and providing oversight and direction.

Two points are critical to delivering this:

  • These agents are integrated into standard tools, like ticketing systems, and processes like sprints 
  • They’re self-organizing and capable of continuous optimization, with no manual prompting required – in fact, the agents can talk to each other. For example, one agent writes code, then provides a highly specific prompt to the testing agent to review it

Human engineers shift from ‘prompters’ to reviewers and collaborators, focusing on refining what Gen AI creates and improving outcomes where needed. In this model, organizations are no longer constrained by variations in individual prompting expertise. Instead, progress moves at the speed of the underlying Gen AI model.

This shift isn’t about removing humans from the loop. It’s about enabling them to focus on the most important work, solving complex problems, driving innovation, and ensuring quality. 

Engineers still play a critical role: 

  • They validate Gen AI outputs and handle edge cases 
  • They guide architectural decisions 
  • They drive strategy and connect technical outcomes to business value 

However, they are no longer bogged down by repetitive tasks or dependent on knowing the perfect prompts. Instead, they can collaborate with Gen AI systems built to integrate, adapt, and improve over time. 

RAISE for Software Product X: Putting Gen AI-assisted development into action 

This isn’t hypothetical. We’ve been building and refining this Gen AI-supported approach to software development with RAISE for Software Product X. 

RAISE for Software Product X is an autonomous, Gen AI-powered software development orchestration platform designed to: 

  • Organize LLMs and agents around specific roles (developer, tester, project manager, etc.) 
  • Integrate directly with software artifacts, tooling, and workflows 
  • Enable continuous, iterative delivery of tasks that support human developers, without relying on perfect prompts 

Conclusion: Let Gen AI adapt to humans 

Prompting will continue to be important. But we don’t need every developer to master it. We need systems that take the best of LLMs, organize them into autonomous agents, and allow them to work collaboratively as part of human-led teams. 

By shifting from embracing Gen AI-assisted development, we unlock the full potential of generative AI at scale, removing friction, accelerating delivery, and empowering engineers to focus on high-value work. 

Discover more about RAISE for Software Product X and experience how it can support your software teams to deliver future growth.