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In January 2022, researchers at Google Brain published a paper that changed how people interact with AI. Jason Wei and colleagues demonstrated that adding intermediate reasoning steps to prompts (a method they called “chain-of-thought prompting”) improved large language model performance on arithmetic, commonsense, and symbolic reasoning tasks (Wei et al., 2022, arXiv:2201.11903). The technique was deceptively simple. Instead of asking an AI for a direct answer, you show it how to think through the problem first. That single insight became one of the landmark moments in a rapidly growing discipline: prompt engineering. And it’s a discipline that matters whether you’re a developer building AI products or someone who just wants ChatGPT to write a better email.
What Is Prompt Engineering?
The Oxford English Dictionary defines prompt engineering as “the action or process of formulating and refining prompts for an artificial intelligence program, algorithm, etc., in order to optimize its output or to achieve a desired outcome; the discipline or profession concerned with this.” That’s the formal version.
Here’s the practical one: prompt engineering is learning how to talk to AI so it actually does what you want.
A prompt is any input you give to an AI model. Could be a question, a command, a paragraph of context, or all three combined. The “engineering” part is where it gets interesting. It’s the deliberate process of structuring, testing, and refining those inputs to produce better, more consistent outputs. Think of it like the difference between asking a coworker “can you help with this?” versus “can you review this draft for factual errors, flag anything unsupported, and suggest corrections with sources?” Same person. Wildly different results. (Note: this analogy is illustrative, not a direct technical comparison.)
Unlike traditional programming, you don’t write code in a formal language. You write in plain English (or whatever language you prefer). But that doesn’t make it easy. The way you phrase a request and the context you provide both directly influence what comes back.
Why Prompt Engineering Matters Now
Three things converged to make this skill critical.
First, the tools went mainstream. ChatGPT launched in November 2022 and brought large language models to millions of non-technical users overnight. Suddenly, everyone from marketers to teachers was writing prompts, whether they realized it or not.
Second, the research matured fast. In 2024, Schulhoff et al. published “The Prompt Report,” a systematic survey analyzing over 1,500 academic papers on prompting. The study cataloged 58 distinct text-based prompting techniques and 40 techniques for other modalities (Schulhoff et al., 2024, arXiv:2406.06608). This wasn’t a niche hobby anymore. It was a documented field with proven methods.
Third, the stakes got higher. As organizations deploy AI for customer support, content generation, code writing, and data analysis, the quality of prompts directly determines the quality of outputs. IBM describes it plainly: prompt engineering bridges the gap between raw queries and meaningful AI-generated responses (IBM, “What Is Prompt Engineering?”). This isn’t just an enterprise concern. Every time you type a question into ChatGPT or ask an AI to draft something, you’re writing a prompt. The difference between a useful response and a useless one is usually the prompt itself.
Core Concepts: How Prompt Engineering Works
Every interaction with an AI model starts with a prompt. The model doesn’t “understand” your words the way another person would. It analyzes patterns in language to predict what should come next based on the input you’ve provided. This means the structure and phrasing of your prompt shape the response in predictable ways. Predictable, though, doesn’t mean obvious. Changing one word in a prompt can produce a completely different output, and nobody fully understands why.
A well-constructed prompt typically includes several components: a clear task or directive, relevant context, constraints on format or length, and sometimes examples of what good output looks like. You don’t always need all of these. A simple question works fine for a simple task. But when you need precision, each component pulls its weight.
The iterative part matters just as much. Prompt engineering isn’t “write once, get perfection.” It’s test, evaluate, adjust, repeat. You try a prompt, examine the output, identify where it fell short, and revise. Over time, you develop an intuition for what works, but the testing never really stops. I think most beginners underestimate this part. The prompt that works on attempt one is the exception, not the rule.
Common Prompting Techniques for Beginners
You don’t need to memorize all 58 techniques from the Schulhoff taxonomy to get started. A handful of foundational approaches cover most beginner use cases. But first, here’s what the difference actually looks like in practice.
A vague prompt: “Write me something about dogs.”
A refined prompt: “Write a 150-word paragraph explaining why golden retrievers are good family dogs. Use a friendly tone. Include one specific health consideration.”
Same AI. Same model. The first prompt returns something generic and unfocused. The second returns something you can actually use. That gap is what prompt engineering closes. (Note: this is a hypothetical example to illustrate the concept, not a measured test result.)
Zero-shot prompting is the simplest form. You give the model a task with no examples. “Summarize this paragraph in two sentences.” It works well for straightforward requests where the model’s general training is sufficient.
Few-shot prompting adds examples. You show the model one or more input-output pairs before asking it to handle a new input. This is useful when you need consistency in tone, format, structure, or logic. The model follows the pattern you’ve demonstrated.
Chain-of-thought prompting encourages the model to reason through a problem step by step before arriving at an answer. Wei et al.’s 2022 research demonstrated this technique’s effectiveness on complex reasoning tasks (arXiv:2201.11903). Though a 2025 study from Wharton’s Generative AI Lab notes that the benefits of chain-of-thought vary by model type and task, with newer reasoning models sometimes gaining little from explicit step-by-step instructions (Meincke et al., 2025, SSRN).
Role-based prompting assigns the model a persona. “You are an experienced editor reviewing this draft for clarity.” This technique activates different response patterns and is widely considered one of the most accessible ways to shape output quality.
Getting Started
Skip the tutorials for now. Pick something you already use AI for and try being more specific. Add context. Specify the format you want. Set constraints on length or tone. Give an example of good output. Then compare the results to what you were getting before.
Prompt engineering rewards curiosity and iteration more than technical expertise. The field continues to evolve (context engineering, which manages the broader information surrounding prompts, is an emerging complement), but the fundamentals stay consistent: clear instructions paired with relevant context, and a willingness to keep refining until the output matches what you actually need.
The best prompt you’ll ever write is probably the next revision of the one you just tested.
Sources Referenced:
- Wei, J. et al. (2022). “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” arXiv:2201.11903. https://arxiv.org/abs/2201.11903
- Schulhoff, S. et al. (2024). “The Prompt Report: A Systematic Survey of Prompting Techniques.” arXiv:2406.06608. https://arxiv.org/abs/2406.06608
- Meincke, L. et al. (2025). “Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting.” Wharton School Research Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5285532
- IBM. “What Is Prompt Engineering?” https://www.ibm.com/think/topics/prompt-engineering
- Oxford English Dictionary. Definition of “prompt engineering.”
