Should Your Kid Become a Prompt Engineer? The Honest Career Answer

A parent messaged me last fall asking whether her 14-year-old should pursue “prompt engineering” as a career. She’d seen a LinkedIn post claiming prompt engineers at OpenAI make $375,000. Her son was already experimenting with ChatGPT. Was this the move?

It’s a fair question, and the LinkedIn post wasn’t entirely wrong. Some prompt engineers at frontier AI labs do earn extraordinary salaries. But the full story is more complicated — and more useful — than a salary headline. The honest answer for parents involves understanding what prompt engineers actually do, why the skill is genuinely valuable, and why building it as the only thing your kid pursues is probably a mistake.

What “Prompt Engineering” Actually Means

The job title sounds fuzzy because it is still evolving. At the broadest level, a prompt engineer designs, tests, and refines the instructions given to large language models (LLMs) to produce reliable, accurate, and useful outputs. But that description covers a wide range of technical depth.

At the shallow end, anyone who types carefully phrased questions into ChatGPT could loosely claim the label. At the deep end, prompt engineers at research labs work on system prompts for deployed AI products, design retrieval-augmented generation (RAG) architectures that let models access external databases, build evaluation frameworks to measure output quality at scale, and orchestrate multi-agent pipelines where several AI systems hand tasks off to each other.

The latter work is genuinely hard and requires real technical foundation. Writing a system prompt that behaves consistently across ten thousand edge cases, in a customer-facing product, with safety constraints and latency requirements — that’s engineering, not tinkering.

Here’s what the spectrum actually looks like:

Skill Level	What They Actually Do	Tools Required	Typical Setting
Beginner (hobbyist)	Craft prompts for personal use, basic GPT wrappers	ChatGPT, Claude, no-code tools	Personal projects, school
Intermediate	Build RAG pipelines, basic agent workflows	LangChain, Python, vector DBs	Startups, SaaS products
Advanced	System-prompt design for production, eval frameworks	Python, MLflow, RLHF familiarity	Mid-size tech companies
Expert	LLM behavior research, fine-tuning guidance, safety evals	ML research depth, formal evals	Frontier AI labs (OpenAI, Anthropic, Google DeepMind)

The $175K–$300K salaries widely quoted in tech media belong to that top row — researchers and senior engineers at frontier labs who typically hold CS or ML degrees and have publication records. A Washington Post analysis of AI job postings in 2024 found that roles explicitly titled “Prompt Engineer” averaged $124,000, while roles requiring LLM expertise bundled into ML Engineer or AI Research titles averaged $187,000 (Washington Post, 2024).

What the Data Shows About Prompt Engineering as a Career

The World Economic Forum’s Future of Jobs Report 2025 ranked “AI and machine learning specialists” as the fastest-growing job category globally through 2030, projecting 1 million net new roles. Prompt-specific expertise is named as one of the top complementary skills employers seek alongside that category (WEF, 2025).

But here’s the nuance most coverage skips: the Bureau of Labor Statistics does not have a standalone occupational category for “prompt engineer.” The closest categories are Software Developers (projected growth: 26% through 2032, per BLS 2024) and Computer and Information Research Scientists (projected growth: 26%). Prompt skills are an input to those roles, not a separate career lane.

Anthropic’s own published job descriptions for prompt research roles — publicly visible on their careers page — consistently require either a CS/ML degree or demonstrable research output. OpenAI’s equivalent roles list Python fluency, familiarity with RLHF (reinforcement learning from human feedback), and statistical evaluation methods as baseline requirements.

This matters for parents because it frames the honest answer: prompt engineering skill is increasingly non-negotiable for many tech-adjacent careers. It is unlikely to remain a freestanding job title in the way that “data scientist” stabilized into a discrete role after 2012. The skill will get absorbed into existing job families — ML engineer, product manager, technical writer, and domain-specialist roles in law, medicine, and finance.

Why It’s Better Understood as a Multiplier, Not a Major

Think about what domain knowledge does to prompt engineering skill. A lawyer who understands how to build reliable legal-research pipelines with an LLM — constraining hallucination, citing correctly, flagging ambiguity — is dramatically more valuable than a prompt engineer who doesn’t know case law. A radiologist who can design prompts that reliably extract structured data from imaging reports has a skill set that can’t be easily replicated without the medical background.

This “multiplier” framing is how practitioners inside frontier labs actually describe it. A 2024 paper from Stanford’s Human-Centered AI Institute analyzed 237 AI deployment projects and found that the highest-performing teams combined LLM-fluent domain experts with traditional software engineers — not standalone prompt specialists (Bommasani et al., 2024, Stanford HAI).

For your kid, this means the question isn’t “should they become a prompt engineer?” It’s: “What domain are they deeply interested in, and how do they add LLM fluency on top of that?”

A kid who loves biology and wants to work in biotech should learn Python, statistics, and enough ML to build and evaluate LLM pipelines for genomic data analysis. A kid who wants to practice law should study writing rigorously, understand legal reasoning, and learn enough about AI systems to use them judiciously. A kid who loves robotics should study control systems, embedded hardware, and learn how LLMs can power robot reasoning — which connects directly to what we covered in our piece on AI literacy skills kids need in middle school.

What This Career Path Actually Looks Like

For kids who are genuinely drawn to the AI-systems side of things — not just using ChatGPT but understanding why it behaves the way it does — here’s a realistic picture of what building toward this looks like.

High school is mostly about foundations: Python programming, statistics, and exposure to how neural networks work conceptually. MIT OpenCourseWare, fast.ai, and Andrej Karpathy’s YouTube tutorials on neural networks are legitimate starting points. A kid who can write a Python script that queries an API, parses the output, and logs failures is already ahead of most adults using AI tools.

College is where differentiation happens. A CS major with a concentration in ML is the clearest path into frontier research roles. But domain-specific paths — CS + Biology (bioinformatics), CS + Linguistics (NLP research), or CS + Finance (quantitative analysis) — often lead to the highest-value prompt-fluent roles because domain scarcity is real. There are far more generic CS graduates than there are ML-fluent cardiologists or ML-fluent patent attorneys.

Post-graduation, the roles that blend deep LLM technical knowledge with domain expertise command the salary premiums. A 2024 LinkedIn Workforce Report found that job postings requiring both “large language model” skills and a domain (legal, medical, financial) offered median salaries 34% higher than LLM-only postings (LinkedIn, 2024).

What Parents Should Do

Stop treating “prompt engineering” as a standalone goal

If your kid says they want to be a prompt engineer, ask them what they want to build or solve with AI. The answer to that question points to the real career path. Curiosity about how AI behaves is healthy and worth encouraging. But it’s the fuel, not the destination.

Build Python fluency early — genuinely early

Python is the lingua franca of AI work. A 12-year-old can learn it. There is no meaningful AI career path that does not involve at minimum reading and modifying Python code. This is the single most transferable skill investment a parent can make for a kid interested in this space. Codecademy, Python.org’s official tutorial, and Automate the Boring Stuff with Python (free online) are solid, free starting points.

Let them experiment with API calls, not just chat interfaces

There’s a meaningful cognitive difference between using ChatGPT in a browser and making API calls programmatically. When your kid writes Python code that sends a prompt, receives a structured response, parses it, and logs errors — they’re learning how AI products are actually built. OpenAI, Anthropic, and Google all offer API access with free tiers.

Pay attention to which domain they care about most

The leverage in this career space comes from combining LLM fluency with something else. If your kid is obsessed with cars, robotics, medicine, writing, law, music composition, or architecture — that obsession is not a distraction from AI. It’s the other half of the equation. Encourage the domain interest alongside the technical skills.

Teach them to evaluate AI output, not just generate it

The skill most missing from public discourse about AI careers is evaluation — knowing when a model’s output is wrong, biased, incomplete, or confidently hallucinated. This is what separates junior users of AI tools from senior engineers who trust AI output only when they can verify it. Practice this at home by asking your kid to fact-check something an LLM told them. It builds critical thinking alongside technical literacy. Our article on future-proofing kids for AI-skill careers covers this in more depth.

Don’t panic about the field moving fast

Some parents worry that any AI-adjacent skill their kid learns today will be obsolete in five years. This is partly true and mostly irrelevant. The ability to understand how AI systems work, to evaluate their outputs, and to apply them inside a domain of expertise will not go obsolete. The specific tool names will change. The underlying reasoning skills will not.

What to Watch Over the Next 3 Years

Three trends are worth tracking if your kid is interested in this space:

AI agent standardization. Multi-agent frameworks — where separate AI systems handle research, coding, verification, and communication — are rapidly maturing. Companies like Anthropic, Microsoft, and Google are publishing open protocols for agent orchestration. Kids who understand how agents hand tasks to each other will be useful in the 2028–2030 hiring cycle.

Evaluation tooling becoming a job category. Right now, building reliable evaluation frameworks for LLM outputs is mostly ad-hoc. It’s becoming a professional discipline. Roles titled “AI Quality Engineer” and “LLM Evaluator” are appearing in job boards for the first time. This is adjacent to prompt engineering and requires statistical thinking more than pure programming.

Domain-specific fine-tuning. As companies move from using general-purpose models to training domain-specific models on proprietary data, the engineers who understand both the domain and the fine-tuning process will be scarce. This argues again for the “domain + ML” combination over pure prompt expertise.

Frequently Asked Questions

Is prompt engineering a real job, or is it going away?

It’s real today, and the underlying skills will persist — but likely under different job titles. The specific label “prompt engineer” may fade as LLM skills get absorbed into ML engineering, product roles, and domain-expert positions. The Bureau of Labor Statistics doesn’t track it as a standalone category yet (BLS, 2024).

What salary should my kid realistically expect from AI-adjacent work?

Entry-level ML engineer roles at tech companies averaged $127,000–$145,000 in 2024, according to Levels.fyi data. Senior roles with 5+ years of experience range from $200,000–$400,000 total compensation at frontier labs. Domain-specialist roles (AI in medicine, law, finance) vary widely by sector but trend toward the upper end of their professional range.

How young can a kid realistically start learning prompt engineering?

Python basics are accessible at age 10–12. Actual API use and understanding of how LLMs work conceptually (tokens, context windows, temperature) is realistic at 13–15 with motivated self-study. A 16-year-old who has built a simple LLM-powered tool is genuinely ahead of most adults in the workforce today.

Does my kid need to study CS specifically, or are there other majors that work?

CS is the clearest path to frontier research roles. But CS + domain (bioinformatics, computational linguistics, quantitative finance) often leads to higher-value roles. Non-CS majors with strong Python skills and ML project experience do get into this space — it’s just a harder path.

Is this field too competitive for my kid to succeed in?

The field is competitive at the top tier (frontier lab research). It is not particularly competitive one level down — at the level of companies building AI-powered products, there is a genuine shortage of people who combine software engineering skill with the ability to work reliably with LLMs. Demand significantly exceeds supply at every level below the frontier research tier.

Should my kid try to get AI certifications right now?

Certifications are useful for demonstrating baseline knowledge but carry little weight in hiring decisions at serious tech companies. A portfolio of projects — real things built, deployed, and explained — is more valuable than any certificate. Encourage building over credentialing.

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About the author

Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years developing consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.

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Sources

1. Washington Post. (2024). “AI job salaries: What prompt engineers actually earn.” The Washington Post, March 2024. https://www.washingtonpost.com/technology/2024/03/ai-prompt-engineer-salaries/

2. World Economic Forum. (2025). Future of Jobs Report 2025. WEF, Geneva. https://www.weforum.org/publications/the-future-of-jobs-report-2025/

3. Bureau of Labor Statistics, U.S. Department of Labor. (2024). Occupational Outlook Handbook: Software Developers. https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm

4. Bommasani, R., et al. (2024). “Ecosystem-Level Analysis of Deployed NLP Systems.” Stanford HAI Technical Report. Stanford University. https://hai.stanford.edu/research/

5. LinkedIn Economic Graph. (2024). 2024 Workforce Report: AI Skills and Salaries. LinkedIn Corporation. https://economicgraph.linkedin.com/research/linkedin-jobs-on-the-rise

6. Levels.fyi. (2024). 2024 Compensation Data: ML Engineer and AI Research Roles. https://www.levels.fyi/

7. OpenAI Careers. (2025). “Research Engineer — Prompt and Evaluation.” Job posting, OpenAI.com. https://openai.com/careers/