Prompt Engineering

At the time, I had no idea that being laid off from Meta would become the Launchpad for an entirely new career one at the forefront of the latest tech wave.

People often ask how I managed to transition into AI prompt engineering, especially when the field was still in its infancy. Back then, even I wasn’t entirely sure what prompt engineering actually entailed.

The role continues to evolve as more companies begin recognizing its value and creating positions to match. So far, I haven’t come across two identical paths into this field. But here’s how I personally made the shift — from working in TV news at CNN and NBC, to news and strategic partnerships at Meta — and carved out my place as a prompt engineer.

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I identified the right opportunity for me

After being laid off, I knew one thing for certain: I wanted to stay in tech. So, I dove deep into research, exploring where my background in journalism and tech partnerships could bring the most value.

I devoured tech news, followed industry chatter, and carefully analyzed job postings to identify roles where my skills could transfer seamlessly. My focus was on finding companies that were not only resilient in the face of ongoing layoffs but also positioned for quick recovery and long-term growth.

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In my search for stability and forward momentum, one name kept coming up — OpenAI and its newly launched ChatGPT. The buzz around it was everywhere, sparking both excitement and concern about what it could mean for the future of work.

As a content creator and former journalist, I was naturally skeptical about letting a bot take over the writing process. But it was clear: a majo

I took calculated risks

I landed a contract role with LinkedIn — a company I had been eager to join — on their news team, where I felt my background would be a natural fit. While the position came with some trade-offs, including a shorter contract and a less senior title, it checked an important box: it was at one of my top-choice companies.

What really stood out was the job description. The content editor role was tied directly to LinkedIn’s newest generative AI initiatives — a space that was just beginning to take off. It felt like a calculated risk, but one worth taking.

Even if the contract didn’t lead to a full-time position, the chance to work hands-on with emerging AI technology could give me a distinct edge in future opportunities.

I tried to be curious and helpful

Even before I officially started the job, I asked about what it was like to work on improving generative AI content — and that’s when I first heard the term prompt engineer. It was a moment that stuck with me.

Once I began the role, which involved editing and evaluating generative AI output, I made it a priority to provide thoughtful, structured feedback. I wasn’t just pointing out flaws — I was identifying patterns and thinking about how to solve larger issues in the prompts or training data. My goal was to show that I understood what made input truly effective, hoping it might lead to deeper involvement.

That instinct paid off.

Today, when I think about scaling a generative AI process, I’m not crafting prompts for individual tasks. I’m designing them to work consistently across dozens or even hundreds of use cases — minimizing errors and keeping results aligned with the intended goal. That means thinking at a systems level and focusing on patterns in the output, not just isolated fixes.

When I talk to people looking to break into prompt engineering, I always suggest starting where they are. Ask yourself: Is your current company experimenting with generative AI? Could you volunteer to help with that project? That’s often where the door first opens.

Do you have skills or domain knowledge that could qualify you to evaluate or annotate model responses? If so, that could be your entry point. Starting small and proving that your input adds value to the prompting process can open doors to a career in prompt engineering.

Building Practical Skills

I genuinely enjoyed the prompting tasks I took on — so much so that I became determined to land a full-time role where this could be my primary focus. One skill that kept appearing in prompt engineering job descriptions was coding, particularly in Python.

Although my current work didn’t require me to write Python scripts from scratch, I often interacted with existing ones. I wanted to understand what those scripts did, what the error messages meant, and how to troubleshoot them myself. My goal was to work more independently, be more efficient, and ultimately become a stronger candidate for future roles.

So, I enrolled in an online Python course to learn the basics. I wasn’t looking to earn a degree — just enough knowledge to navigate the technical side of my work with confidence. Very quickly, I picked up the language and terminology, which made communicating with engineers easier and showed my team I was invested and capable.

That effort paid off. It helped me pass basic coding assessments in job applications and played a key role in landing my current position as a Prompt Director at an AI startup.

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Looking back, the most valuable lesson — whether in prompt engineering or any career path — is this: never stop learning and always stay open to new possibilities.

Kelly Daniel is a leader in AI prompt engineering, known for deploying AI solutions at the enterprise level. As Prompt Director at Lazarus AI, she specializes in developing innovative prompting strategies and exploring new applications for large language models and cutting-edge agentic systems. She’s also an instructor for CNBC’s online course, How to Use AI to Be More Successful at Work.