Hey Freelance Friends!

Two years ago, when I first explored the rapidly evolving world of generative AI, it seemed like science fiction. Today, it is changing how clients work with freelancers. Surveys from 2025 show that roughly three‑quarters of freelancers have already integrated AI tools into their workflows, and about one‑fifth use generative AI. Those who have embraced these tools are seeing tangible benefits: in one survey of top‑tier freelance technologists, 80 percent said generative AI increases their earning potential and 91 percent believe AI skills make them more attractive to clients. This shift isn’t just happening at the high end of the market. Platforms such as Upwork and Fiverr report that demand for AI‑related services has skyrocketed, while traditional writing and translation gigs are declining.

Being able to work with generative models isn’t optional anymore. It’s now part of the basic toolkit for anyone who sells their skills and time. One particularly valuable capability is prompt engineering, the art of crafting instructions that guide AI models toward useful, accurate responses. According to an Indeed analysis cited in a 2025 review, learning generative‑AI skills like prompt engineering can increase salaries by up to 47 percent, making it one of today’s highest‑paying technology skills. A separate PwC study noted that AI‑related jobs can command a wage premium of about 25 percent. Even outside full‑time employment, prompt engineers can earn six‑figure salaries or consult remotely for high rates; an August 2025 job‑market report found that U.S. prompt engineers earn an average base salary of about US$123k and often work fully remotely.

When I started building bots for clients, the resources available were scattered and often technical. I found a clear path with Learn Prompting’s Introduction to Prompt Engineering course. It teaches how to craft effective prompts and explains why large language models respond the way they do. The course notes that large models like ChatGPT and Claude are powerful tools for automating tasks and augmenting decision‑making, but they require carefully designed language instructions to produce useful output. Prompt engineering is defined not as coding but as “crafting effective instructions” for AI tools.

The curriculum is concise, a self‑paced program designed to be completed over three days, and uses bite‑sized video lessons interspersed with quizzes and hands‑on assignments. Modules cover the anatomy of a prompt, few‑shot prompting, chain‑of‑thought strategies, refining prompts and problem-solving, and real‑world prompting exercises. Beyond techniques, the course delves into topics that matter for freelancers: managing bias and hallucination in AI output, improving productivity by using AI as a personal assistant, and following ethical guidelines.

One of the most valuable parts for me was understanding why language models behave the way they do. The lessons on LLM functionality explore concepts like tokenization, context windows, and model limitations, helping you predict when an answer might be missing important context. This kind of grounding is essential when you’re using AI to draft proposals or assist with research. You need to know how to spot and correct mistakes, not just cut and paste output.

The course is taught by Sander Schulhoff, the founder of Learn Prompting and a machine‑learning researcher. He created the first open‑source prompt‑engineering guide, which reached more than three million learners. Schulhoff led a team that produced Prompt Report, a 76‑page survey analyzing over 1,500 academic papers and more than 200 prompting techniques. Having an instructor who actively researches the field keeps the material grounded in current best practices.

Learn Prompting offers a Plus subscription that gives access to the full library of 15+ courses, including modules on generative‑AI agents, retrieval‑augmented generation, and AI safety, and provides certificates of completion that can be shared with clients or on LinkedIn. If you’re just beginning, the course can be audited for free.

The freelance marketplace is already feeling the impact of AI adoption. An October 2025 report on AI’s impact on freelancers found that about 73–75 percent of freelancers globally have incorporated AI into their work and roughly 20 percent use generative AI regularly. Those who complement AI instead of competing against it are thriving; they automate routine tasks and move into specialized roles. Prompt engineering skills fit squarely into this opportunity. Demand for AI‑related freelance services on platforms like Upwork has increased over 1,000 percent since 2023, and high‑earning freelancers report that mastering AI and prompt engineering significantly boosts their productivity and value.

As AI advances, clients will expect freelancers not only to use tools like ChatGPT but also to integrate them responsibly. This means being able to draft effective prompts, iterate on responses, evaluate and correct results, and navigate ethical issues such as bias and attribution. Introduction to Prompt Engineering prepares you for those tasks by teaching prompt writing and optimization, problem‑solving strategies, and ethical AI practices.

It’s also worth noting that prompt engineering is a human skill. A recent overview of AI prompt‑engineering careers described it as “programming in natural language” and emphasised that prompt engineers translate vague business goals into actionable AI outputs. The job often involves cross‑functional collaboration and safety monitoring. Those communication and critical‑thinking abilities, traits that many freelancers already possess, are often more important than advanced coding skills.

My own experience illustrates how mastering prompts pays off. When I began experimenting with generative models, I relied on pre‑written templates. The results were hit‑or‑miss, and I often had to rewrite responses by hand. After completing Learn Prompting’s course and practising the techniques, few‑shot examples, chain‑of‑thought, and iterative refinement, I started building custom bots for clients. These bots automate research, generate drafts, and even outline proposals. They don’t replace my work; they extend it, freeing time for strategy and editing.

This newsletter’s bot system, in fact, started as a prototype built immediately after I took the course. By combining the course’s prompting framework with our domain knowledge, we developed bots that now assist with summarising research and brainstorming ideas. Learning to speak AI fluently was the turning point.

Understanding how AI systems work also teaches you when not to deploy them. Many technology vendors are guilty of “AI‑washing”, attaching an AI label to products that use little or no machine learning at all. A May 2025 industry analysis describes AI‑washing as a tactic that misleads investors and customers; companies may exploit the hype even though the technology is not pivotal to the product. The article warns that AI‑washing thrives because most people are unaware of what AI can actually do and how it differs from long‑standing technologies; to tell the difference you need familiarity with existing tools and an understanding of what machine learning uniquely enables. A compliance report notes that 40 percent of European start‑ups that called themselves “AI companies” in 2019 used virtually no AI at all. Without a basic understanding of AI, you’re more likely to buy into marketing rhetoric and invest in solutions that add little value.

AI literacy also encourages sustainable technology choices. Generative AI models are energy‑intensive: data centres already consume about 1.5 percent of the world’s electricity and generate roughly 1 percent of global carbon emissions. Training models like GPT‑4 spins up thousands of GPUs and uses vast amounts of water to cool the hardware. MIT researchers warn that training and deploying generative models demands “staggering” amounts of electricity, creating large carbon‑dioxide emissions, and that each ChatGPT query can consume several times more electricity than a simple web search. These impacts matter: using AI when a simpler tool would suffice wastes resources and exacerbates environmental costs.

Educated freelancers can recognise when AI genuinely adds value and when a task might be better handled by traditional software or human judgment. Making these distinctions reduces overreliance on energy‑hungry models, keeps projects lean, and cuts down on the unnecessary emissions associated with running complex algorithms. For example, the BairesDev piece advises that firms should critically evaluate whether their AI investments truly meet their needs or if funds would be better spent on more innovative solutions; if ChatGPT is merely functioning as an advanced search tool, 100 paid licenses may be wasteful. Responsible use isn’t about rejecting AI; it’s about deploying it where it offers clear benefits and avoiding it when it doesn’t.

Freelancing in 2025 is no longer just about your craft; it’s about how effectively you can collaborate with powerful AI tools. Most freelancers are already using AI, and those who build prompt‑engineering proficiency are securing higher fees and more interesting projects. Learn Prompting’s Introduction to Prompt Engineering offers a concise, practical way to develop that capability. If you’re looking to future‑proof your freelance career, start by learning to speak AI.

— The Profreelance Crew