How I rebounded after getting laid off from Meta

I didn’t know it at the time, but getting laid off from Meta was about to be my springboard to a new career — one at the cutting edge of the newest tech obsession.

I frequently get asked how I pivoted into AI prompt engineering, particularly when it was so new. At the time, most people — including me! — didn’t know what prompt engineering even was.

The job is still evolving as companies open roles and integrate these skills. And I haven’t heard any two identical origin stories yet. But here are a few steps I took as I changed careers from TV news at CNN and NBC, and then news and strategic partnerships at Meta, to establish myself as a prompt engineer.  

After the layoff, I was sure I wanted to stay in tech, so I spent a lot of time researching where my journalism and tech partnerships experience might be valued.

I consumed every bit of tech news gossip and examined companies and job descriptions for transferable skills. I was focused on finding companies that might be well-positioned to avoid the ongoing wave of layoffs, or at least bounce back quickly.

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I looked for stability and growth.

This meant I heard a lot (a lot) about OpenAI’s newly launched ChatGPT, and all the changes people hoped and feared it might bring. As a content creator and former journalist, I’m hesitant to hand the literary reins over to a bot. But I could see there was a very real market shift coming — and that was an opportunity for me.

I found a contract role at LinkedIn, a company I was very eager to work for, on the news team, where I would surely fit in. There were some drawbacks, like the short contract duration and less senior role. 

But it was at one of my target companies, and the job description made it clear that this content editor role would be focused on the platform’s newest generative AI projects. That struck me as a risk worth taking.

The exposure to the new and rapidly evolving technology had the potential to give me an advantage on other job applications, even if this contract wasn’t extended or converted to a full-time role.

Before I even had the job, I asked about what it was like to work on improving the quality of generative AI content. The hiring manager’s response was actually the first time I heard the term prompt engineer!

As I worked on editing and rating the generative AI output, I made sure my feedback was clear and tried to identify themes I saw overall. I focused on what I thought would help solve the bigger problems in the prompts or training, and hoped demonstrating an understanding of useful input might open a door for me to get more involved.

This hunch panned out well. Now, when I think about making a generative AI process work at scale, I don’t write a prompt for every individual task. I want it to work dozens or hundreds of times with very few errors or deviations from the goal, which means I have to focus on prompts that address the themes or trends in the output. 

When I talk with someone who hopes to move into prompt engineering, I always tell them to think about where they can start right now: 

• Is their current company implementing any generative AI projects they could offer to help with? 
• Do they have skills or knowledge that might make them qualified to score or annotate model responses? 

If you can start small and prove your input is valuable to the prompting process, you can create opportunities for yourself in prompt engineering.

I absolutely loved the prompting assignments I took on, and I soon became determined to secure a full-time role where this work could be my main focus. One skill I kept seeing in prompt engineer job postings was some level of proficiency in coding, specifically with Python.

I didn’t need to write Python scripts for the work I was already doing, but I did work with some existing scripts. I wanted to understand how they worked and what the errors meant. I wanted to become more self-sufficient and work more efficiently, without waiting for an engineer’s help. I wanted to make myself a stronger candidate for future roles.

So I took an online course to learn Python basics, hoping I could learn enough without fully hitting pause to go back to school for a degree. I quickly picked up the lingo that made it easier for me to talk to engineers and it showed the team I was committed and valuable.

It also gave me a leg up in my job applications, helping me pass simple coding tests and ultimately land my current role as a prompt director for an AI startup. 

Looking back, I’d say the biggest lesson for any career, and wherever prompt engineering takes me, is to always keep learning and stay open.

Kelly Daniel is a leader in AI prompt engineering with extensive experience implementing AI solutions for enterprise businesses. As Prompt Director for Lazarus AI, she develops prompting techniques and new applications for LLMs and cutting-edge technologies like agentic models. She is an instructor in CNBC’s online course How to Use AI to Be More Successful at Work.

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