The Prompt Engineer in 2025

As we experience the rapid evolution of artificial intelligence, the software development landscape is shifting dramatically. Over the past few months - from late 2024 into 2025 - AI agents have moved from experimental novelties to indispensable tools that enhance productivity and strategic decision-making. Looking ahead to 2030, it's clear that "traditional software development" will no longer be enough for the average engineer to stay relevant. Instead, the role of the developer can be reframed: you must develop technology to develop the business (e.g. bring business value).

In this environment, the concept of a "prompt engineer" emerges. This new role bridges the gap between customer needs and technical delivery.

Think of the prompt engineer as someone who talks to customers and figures out, at a high level, what they need (like a PM); then goes and talks to their team of AI agents to actually build the software (like an EM); then checks for best practices, debugs it, makes it compliant, and releases it to production (like a SWE).

The Emergence of AI Agents

Recent advances in AI have created the concept of agents - abstracted, autonomous AI systems - which can deal with tasks once reserved for human developers. Instead of spending an entire week on a single JIRA ticket and saying "nothing from my end" the following standup, teams will start to expect immediate solutions. Not only will your EM expect you to write software more quickly, he/she will expect that this software is in line with business goals. This means you are both the engineer, the PM, and the "team" (agent? robot?) lead.

AI agents are not simply tools for lousy code generation - they can act as partners that interact with interfaces, act as customers, and sniff out growth areas. When properly managed, can accelerate development cycles by 10-100x and enhance solution quality. You don't need to worry about them spending half a sprint on reading outdated docs instead of coding. You don't need to sigh and ask "how can I help?" when they complain about "having non-stop meetings". (translation: three 30-minute meetings back to back)

Understanding Prompt Engineering

At its core, prompt engineering is just the process of writing really, really detailed and precise instructions that can accurately guide AI systems into doing what they need to do. Like generating effective code and solutions.

However, the scope of this role in the future, will extend far beyond simply "asking" AI for code. The discipline requires a deep understanding of the problem domain, a grasp on clear communication, and a strategic mindset that aligns technical solutions with business goals. Prompt engineering is less about micromanaging the code line-by-line and asking "why did we choose PostgreSQL instead of MySQL?"; instead, it is entirely about defining the problem, directing AI planning and analysis, and verifying that the generated output meets the business requirements.

The Evolving Role: From Software Writer to Agent Manager

Strategic Problem Identification

The prompt engineer's journey begins with understanding the customer’s pain points. This involves engaging with stakeholders, analyzing market trends, and identifying where technology can create the most impact. Instead of waiting for a detailed ticket to be assigned, the prompt engineer is the one that 1) creates a ticket, 2) adds details, 3) triages it, 4) assigns it to him/herself, 5) gets an AI to fix it, 6) tests & releases it, and 7) checks if customers are happy with the result.

AI Collaboration In Practice

The first step in prompt engineering is finding a technical problem to solve (yeah, that's it). Once the problem is defined, the next step is to formulate a strategy for the AI partner (agent? llm? Claude3.5.67.743?). This requires:

• Crafting precise prompts: Prompts must be unambiguous and detailed enough to instruct the AI on what needs to be built.

• Debugging and validation: Post-generation, it is crucial to review and refine the AI's output. Ensuring that the code not only works but also aligns with best practices. Specifically, you may want to check for things like deadlocks, personal data compliance, and "DROP SCHEMA public CASCADE" statements.

• Iterative improvement: software engineering is still a continuous process. Prompt engineering is the same. Each iteration provides insights that lead to more effective prompts and better final products.
  

This process essentially transforms the role of the engineer from a hands-on coder to an orchestrator of AI-driven processes - a role that may soon be called any number of crazy things, from "agent manager" to "AI executive."

From People Management to Agent Management

In the traditional model, a manager oversees a team of developers. In the emerging AI paradigm, the prompt engineer functions as an "agent manager," guiding a suite of AI tools (or a team of agents) that generate, test, and refine code. This role requires both technical expertise and the ability to communicate complex ideas in a way that both AI and human stakeholders can understand.

Surprisingly, this is kind of a tall order nowadays. Most engineers don't even know how to have a coffee chat, let alone explain how Kubernetes works.

Best Practices in Prompt Engineering

1. Develop an Understanding of the Business

As a software engineer, this about the scariest thing there is. Successful prompt engineering starts with a clear understanding of the business context. Engage with customers and stakeholders to determine:

• What specific problems need solving?

• How will the solution impact business performance?

• What metrics will be used to measure success?

2. Get Good at Communicating Clearly

In #1 I lied, this is about the scariest thing there is. Some might say that crafting clear and effective prompts is an art. I wouldn't go that far; but make sure to use clear, concise language that leaves little room for ambiguity. Consider the following:

• Contextual clarity: Include necessary background information to guide the AI. This can be code or abstract knowledge.

• Step-by-step instructions: Break down the task into manageable components. Imagine you're mentoring an intern who is really good at Googling but doesn't know what Python is.

• Iterative feedback: Regularly review the AI’s output and refine your prompts based on what works and what doesn't. You can be rude if you want. :)

3. Embrace Iterative Development

The initial output may not be perfect. Implement a robust feedback loop:

• Review and debug: Thoroughly test the AI-generated code. This is probably the hardest part unless you have a dedicated QA.

• Refinement cycles: Use the results to refine your prompts for better outcomes. Make sure to be patient, or you risk confusing your genius intern.

• Documentation: Keep a record of prompt iterations and the corresponding outcomes to build a knowledge base for future projects.

4. Integrate Cross-Disciplinary Skills

Most people can be mediocre prompt engineers. It doesn't take a genius, sorry. However, in order to excel, you should have:

• Technical knowledge: Understand the underlying principles of the software and systems you’re guiding.

• Business context: Align your technical outputs with overarching business goals.

• Focus on the user: Consider the end-user’s needs to ensure that the final product delivers tangible value.

Challenges and Considerations

While it sounds like a great approach to building software, there are challenges to consider:

• Quality assurance: AI-generated code can be prone to subtle errors. A rigorous testing and validation framework is essential.

• Ethical considerations: As with any AI application, ethical considerations—including bias and transparency—must be addressed.

• Skill transition: Traditional developers may need to adapt to new roles that emphasize strategy and AI management over hands-on coding. Unfortunately, this is the toughest thing to sell them on in 2025. More than anyone else, big-tech is scared of things it doesn't understand. (AHHH ETHEREUM!!)

Several industry analyses have highlighted these challenges, noting that while the technology is promising, the human element in supervising and refining AI outputs remains critical. You can't completely zone out.

The Future Outlook: A Roadmap to 2030

Looking forward, the integration of AI agents into software development will only deepen. By 2030, engineers will need to deliver more technical and business value in a day than they do in a week today. The evolution of the "software engineer" to the "prompt engineer" will likely start with:

• Increased automation: AI agents will become more autonomous, handling more complex tasks with minimal human intervention.

• Enhanced collaboration tools: New platforms and tools will emerge to streamline the interaction between human engineers and AI agents.

• Data-driven decision making: Advanced analytics will help engineers refine their strategies and improve AI's in-context performance continuously.

• Expansion of AI literacy: As the role becomes more central, educational programs and professional development courses focused on prompt engineering will proliferate. (That's what I'm trying to do!)

Industry leaders and thought innovators are already emphasizing that the ability to seamlessly integrate AI into business strategy is a key competitive advantage (not really a differentiator anymore). The role of the prompt engineer is poised to become the most - if not the only - sought-after position in technology.

Subscribe to my content to become one of the first prompt engineers.

Don't get left behind.