Prompt Engineering: Skills you need to land the job (and how to actually learn them)
So, you’ve heard the hype. Prompt engineering is the it job right now. AI is exploding, companies are scrambling for talent, and suddenly everyone wants to be the one whispering sweet nothings into the digital ears of ChatGPT, Claude, and Midjourney.
But what does a Prompt Engineer actually do and what do you need to know?
If you're picturing a job where you just type "make this better" into ChatGPT and cash a six-figure paycheck—sit down, bestie. It’s more than just clever phrasing and good vibes. But, don’t worry, I’ve done the legwork. I browsed through heaps of job listings and pulled the most common skills you’ll need to know to land a job in Prompt Engineering:
1. Natural Language Processing (NLP)
What it is: NLP is the reason your AI tool understands what you mean instead of just taking your words literally. It covers how machines process, understand, and generate human language. It's what makes the difference between "write me a poem" and "generate romantic-themed output with rhyme and meter" actually... working.
Why you need to know it: Without NLP knowledge, you’re just throwing words at a wall to see what sticks. Understanding how LLMs parse grammar, context, and semantics gives you the power to shape better outputs. Basically, you’ll sound like a wizard when you nail the right phrasing.
Tools you’ll need: spaCy, NLTK, OpenAI tokenizer, LangChain
Real world job task: Rewriting prompts so the AI stops misunderstanding user intent and stops generating nonsense.
Prompt you might use: “Rewrite this customer support reply to sound more empathetic, ensuring it answers all parts of the original question.”
Learn it: Take a crash course in NLP on Coursera or read up on Hugging Face’s NLP guides.
2. Basic programming (Python is #1)
What it is: You don’t have to be a full-on developer, but you do need to know how to wrangle APIs, debug scripts, and automate repetitive stuff. Python is the prompt engineer's BFF.
Why you need to know it: Prompt engineering isn’t always manual. Python lets you automate prompt chains, clean input/output, or build chat interfaces. It’s the glue that holds all your prompting experiments together.
Tools you’ll need: Python, Jupyter Notebooks, VS Code, Git
Real world job task: Automating daily prompt testing and logging results.
Prompt you might use: “Create a Python script that tests 20 prompt variations and returns output length and sentiment score.”
Learn it: Python for Everybody (free), Automate the Boring Stuff with Python.
3. Model-specific knowledge (ChatGPT, Claude, Gemini, etc.)
What it is: Each model has its own quirks, strengths, and weird habits. What works on ChatGPT might flop on Claude. You need to know which tool to use and when.
Why you need to know it: Companies expect you to pick the right model for the job—not waste tokens figuring it out. Understanding differences in capabilities, context windows, and formatting helps you prompt smarter.
Tools you’ll need: ChatGPT, Claude, Gemini, Perplexity, model comparison sheets
Real world job task: Choosing the best model to generate multilingual responses or answer math-heavy queries.
Prompt you might use: “Test this prompt across GPT-4 and Claude 3 to compare tone and factual accuracy.”
Learn it: Use them regularly. Read model cards and changelogs on OpenAI, Anthropic, and Google sites.
4. Retrieval-Augmented Generation (RAG)
What it is: RAG is when the AI pulls in external info (like docs or databases) to answer your prompt more accurately. It's like giving the AI your cheat sheet.
Why you need to know it: Many tools now integrate RAG to reduce hallucination. You’ll need to structure prompts and data so the model can access and understand it.
Tools you’ll need: LangChain, LlamaIndex, Pinecone, ChromaDB
Real world job task: Connecting internal docs to a chatbot so it gives factually correct answers to customer questions.
Prompt you might use: “Given this document, answer the user’s question as accurately as possible, citing the source.”
Learn it: LangChain tutorials, YouTube RAG deep-dives
5. Large Language Models (LLMs)
What it is: The tech behind ChatGPT and friends. These are massive neural networks trained to predict the next word based on billions of examples.
Why you need to know it: Understanding how LLMs learn and make predictions helps you debug weird outputs, avoid hallucinations, and prompt more effectively.
Tools you’ll need: OpenAI API, Hugging Face, TensorFlow (for the ambitious)
Real world job task: Explaining to your boss why GPT is making stuff up and how to fix it.
Prompt you might use: “Summarize this article using factual accuracy only—don’t speculate or invent information.”
Learn it: Blogs like The Gradient, YouTube explainers on transformers
6. Data analysis & cleaning
What it is: It’s making sure your data isn’t garbage before feeding it into the model. This includes removing duplicates, errors, and inconsistencies.
Why you need to know it: Garbage in, garbage out. Clean input = clean output. Your prompts and training data must be squeaky clean for quality results.
Tools you’ll need: pandas, NumPy, Excel (yes, still), regex
Real world job task: Cleaning a CSV of product descriptions before feeding them into your product-generating bot.
Prompt you might use: “Create a regex that removes HTML tags from this text field.”
Learn it: DataCamp, Kaggle, YouTube data cleaning tutorials
7. UX/UI awareness
What it is: Understanding how your prompts and outputs fit into a product’s user experience. Basically, will a normal person find this usable?
Why you need to know it: No one wants to use a chatbot that gives essay-length replies to yes/no questions. Make your prompts user-friendly and context-aware.
Tools you’ll need: Figma, user testing tools, Hotjar, Notion
Real world job task: Redesigning chatbot responses to fit inside a mobile app UI.
Prompt you might use: “Shorten this to fit inside a notification bubble while keeping the helpful tone.”
Learn it: UX crash courses, Product Design on Coursera
8. Prompt optimization & evaluation
What it is: Making prompts better. Testing variations. Evaluating what works. Rinse and repeat.
Why you need to know it: Prompt engineering is this. You’re not doing your job if you’re not improving, testing, and refining prompts based on real feedback.
Tools you’ll need: PromptLayer, GPT Pilot, spreadsheets
Real world job task: A/B testing five prompt versions to improve response accuracy by 15%.
Prompt you might use: “Evaluate the tone and clarity of each response on a scale of 1 to 10.”
Learn it: Prompt evaluation docs from AWS, your own experimentation
9. Ethical awareness / Bias mitigation
What it is: Being responsible with what you build. Bias creeps into LLMs easily, and it’s your job to catch it.
Why you need to know it: The consequences of biased outputs can be huge. You need to be proactive in spotting and fixing problems.
Tools you’ll need: Fairlearn, AI Fairness 360, prompt red teaming
Real world job task: Reviewing chatbot responses for gender or racial bias.
Prompt you might use: “Analyze this output for biased language or stereotypes.”
Learn it: Mozilla’s Responsible AI guide, Ethics in AI courses
10. AI toolstack familiarity (OpenAI, Midjourney, ElevenLabs, etc.)
What it is: There are tools for everything. Text. Images. Video. Sound. Knowing which ones do what = superpower.
Why you need to know it: The best prompt engineers mix tools to make magic. You want to be the one who says, “I can totally generate that campaign in three steps.”
Tools you’ll need: OpenAI, Midjourney, ElevenLabs, Runway, PlaygroundAI
Real world job task: Creating a branded explainer video with AI-generated script, voice, and visuals.
Prompt you might use: “Write a 30-second product script in a casual tone, then generate visuals in Midjourney using this concept.”
Learn it: YouTube AI creators
11. Communication & documentation
What it is: Writing clean instructions, documenting experiments, and not making your team guess what your prompt did.
Why you need to know it: Being good at prompting isn’t helpful if no one understands your process. This is how you make your work repeatable and valuable.
Tools you’ll need: Notion, Confluence, Loom, Google Docs
Real world job task: Writing clear documentation for a client-facing prompt library.
Prompt you might use: “Summarize this prompt’s purpose, context, and intended use for teammates.”
Learn it: Write the Docs, Tech Writing 101, document your own experiments
12. Conversation design
What it is: Crafting multi-turn interactions that feel natural. Think chatbot scripts, not one-liners.
Why you need to know it: People don’t talk like robots. You need to anticipate follow-up questions, misunderstandings, and tone shifts.
Tools you’ll need: Voiceflow, Botpress, ChatGPT custom instructions
Real world job task: Designing a multi-step onboarding flow for a chatbot.
Prompt you might use: “Write a welcome message that asks a user their name, email, and what they’re looking for—in three turns.”
Learn it: Conversation Design Institute, Dialogflow docs
13. Multimodal prompting
What it is: Prompting across text, image, audio, and video. It's where prompting gets really wild.
Why you need to know it: We’re moving beyond text-only. The future of prompting is multimodal. It’s how AI becomes truly creative.
Tools you’ll need: GPT-4o, Gemini, Midjourney, ElevenLabs, D-ID
Real world job task: Creating a narrated explainer with matching visuals for a product demo.
Prompt you might use: “Generate a cartoon-style image of a robot watering plants and write a playful voiceover script.”
Learn it: OpenAI use cases
14. Experimentation and iteration
What it is: You try stuff. It breaks. You tweak it. It works better. That’s the job.
Why you need to know it: Prompt engineering isn’t a one-and-done deal. It’s trial, error, rinse, repeat. Every prompt is an experiment.
Tools you’ll need: Prompt engineering notebooks, A/B testing tools, ChatGPT Pro
Real world job task: Building and refining prompt chains for a customer support bot.
Prompt you might use: “Iterate on this prompt to reduce repetition and improve clarity across three versions.”
Learn it: Your own projects. Just start messing with prompts and track results.
Lastly, here are some blogs I wrote that are very helpful!
All about LLMs (aka: what even is ChatGPT’s brain?)
How to train an LLM (as a Prompt Engineer, not a soccer coach)
What is a Prompt Engineer? The tech job everyone wants (but no one can define)
The rise of AI agents: What they do and why they matter
AI bias: When your chatbot plays favorites (and how we’re fixing it)
How chatbots are built: The tech wizard behind the AI curtain
What is Agentic AI?Understanding the future of autonomous AI
A boomer’s guide to Machine Learning
Prompting for dummies: What the heck is prompting and why should I care?
Examples of advanced AI prompting techniques
Explain Large Language Models to me like I’m a moron
Master AI prompting with these great courses!
Final thoughts
Don’t worry! You won’t need to know everything about all of these skills. Just mastering a couple skills could easily land you the job. Always apply even if you don’t have 100% of the skills the job listing requires.
Prompt engineering is part linguist, part scientist, part creative weirdo. It’s not about typing magic words and getting perfection—it’s about understanding why those words work and how to tweak them when they don’t.
The more you play, test, and learn, the better your instincts get. The tools change fast, but the foundational skills you just read? They’re what make you valuable no matter what.
Now go forth and prompt something weird.
