For a brief moment in time, prompt engineering felt like the most valuable skill in the AI economy. People were earning serious money simply for knowing how to phrase a question in a way that got a useful answer from ChatGPT. Courses appeared. Job titles appeared. The phrase “prompt engineer” started showing up on LinkedIn profiles and hiring boards.
Then something shifted.
In June 2025, Andrej Karpathy — former research scientist at OpenAI and former Senior Director of AI at Tesla — posted a message that reframed the entire conversation. “People associate prompts with short task descriptions you’d give an LLM in your day-to-day use,” he wrote. “When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step.”
Shopify CEO Tobi Lütke amplified the idea within days, calling context engineering the highest-leverage skill for anyone working seriously with AI. Anthropic published a reference document titled “Effective context engineering for AI agents.” By early 2026, Gartner had added its own formal definition. A comprehensive academic survey analysing over 1,300 papers formalised it as a distinct discipline within a month of Karpathy’s post.
The shift from prompt engineering to context engineering is not a semantic argument about terminology. It is a practical description of what actually separates people who get consistently excellent AI output from people who keep getting generic, unreliable results — and wondering why.
What Actually Changed, And Why It Matters
To understand why this shift happened, it helps to understand what the original limitation of prompt engineering was.
Early AI tools rewarded clever wording. If you knew how to frame a question in the right way, you could get the model to produce something genuinely useful. This was a real skill and it had real value. But as models became significantly more capable, the limiting factor was no longer how cleverly you phrased the instruction. It was how much relevant context the model had access to when it tried to answer.
Think of it using Karpathy’s own analogy. An LLM is like a CPU. Its context window — the total amount of information it can hold in focus during a single session — is like RAM. Your job as someone using AI seriously is like the job of an operating system: deciding what gets loaded into that working memory before the model starts generating anything.
The Difference in Plain Terms
Prompt engineering focuses on the instruction itself — the phrasing, structure, and wording of the question you ask. Context engineering focuses on everything that surrounds that instruction: the information the model has access to, the role it has been given, the examples it can draw from, the goals it understands, and the constraints it operates within.
A prompt is a question. Context is the environment in which that question gets answered. A brilliant question in an empty room produces a generic answer. The same question in a room stocked with the right reference material, the right examples, and a clear understanding of what you are actually trying to achieve — that produces something useful.
This is why someone asking Claude or ChatGPT “write me a marketing plan” gets something generic, while someone who has set up their brand voice, shared examples of their best past campaigns, defined their target audience, and specified the format they need — gets something that looks like it was produced by a skilled colleague who has been working with them for months.
The Five Things You Need to Understand to Use This Skill
One: Build Systems, Not Individual Prompts
The biggest practical shift in how serious AI users work is moving from one-off prompts to persistent systems. Instead of starting from scratch every time you open a new conversation, you build a context-rich environment that the AI operates within consistently.
In practice, this means feeding the AI background information before the task begins. Your brand voice. Your audience. Examples of work you want it to emulate. The goal the output needs to achieve. The format you need it delivered in. When all of this exists before you ask the actual question, the quality of what comes back changes fundamentally.
Use tools that support persistent memory or custom instructions. Claude’s Projects feature, ChatGPT’s custom instructions, and system prompts in API implementations all allow you to set this context once and have it inform every session that follows. You are no longer prompting. You are designing a working environment for a capable assistant who remembers who you are and what you are building.
Two: Structure and Guide Rather Than Prompt and Hope
Most people still treat AI like a vending machine. Put something in, get something out. When the output is mediocre, they rephrase the prompt and try again — which is like asking a confused colleague the same question twenty different ways and hoping the wording eventually clicks.
The more effective approach is to guide the AI through the task structure before asking it to produce anything. Define the role it should take. Provide examples of what good output looks like in your specific context. Clarify the goal and the constraints. Set the format. Then ask for the output.
Here is a concrete example. Instead of opening a conversation and typing “write social media posts for my business,” build a context where the AI knows it is helping a small Nigerian skincare brand aimed at millennial women, knows your tone is friendly and direct rather than corporate, knows your goal is to drive DM inquiries rather than vanity engagement, and has seen three examples of your best performing posts. Then ask for five new posts. The difference in what you get back is not marginal. It is the difference between something you can post and something you need to rewrite from scratch.
Three: Give AI the Knowledge It Needs to Think Properly
This concept is called knowledge scaffolding — building a mental map for your AI rather than asking it to operate on assumptions and general training data alone.
When you ask AI to help you write a business proposal without providing any supporting material, it produces a generic proposal based on what business proposals generally look like. When you give it your previous proposals, your company profile, relevant industry data, your pricing structure, and the specific client context — it produces something that reflects your actual situation rather than a template.
The practical implementation is straightforward. Create what you might call context packs: small collections of your most important reference material for different types of tasks. Your writing context pack might contain a style guide, three examples of your best work, and a brief description of your audience. Your client communication context pack might contain your standard terms, your product or service descriptions, and examples of how you handle common questions. Load the relevant pack at the start of each session before you ask for anything.
This is especially important given how AI hallucination works. When a model lacks the specific context it needs, it fills the gap with plausible-sounding information that may be completely wrong. Giving it the right knowledge does not just improve quality — it reduces the risk of confidently wrong output. Understanding how AI models actually process information helps you appreciate why this matters so much in practice.
Four: Consistency Comes from Stable Environments, Not Better Prompts
If you have ever noticed that your AI-generated content feels like it was written by different people from session to session — on-brand one day, completely off the next — you have experienced the consequence of inconsistent context rather than inconsistent prompting.
The model has no memory between sessions unless you give it one. Each new conversation starts from zero unless you have built a system that loads your context automatically. This means every session where you start with a fresh prompt and no supporting context produces output shaped by the model’s general training rather than your specific voice, your specific audience, and your specific goals.
Context engineering solves this by creating a stable working environment. Your style guide tells the model how your brand communicates. Your audience definition tells it who it is speaking to. And our examples show it what the target quality looks like. Once these are consistently loaded, the output consistency follows naturally — not because you found the perfect prompt, but because the model is always operating in the same environment.
Five: The Future AI Expert Is a System Builder
Karpathy described this directly in his follow-up commentary. He argued that calling a well-designed AI context a “prompt” trivialises something that is actually a rather complex component in a serious application. You prompt an AI to tell you why the sky is blue. You build contexts meticulously for AI to solve your specific, customised tasks reliably over time.
The evolution of AI expertise is moving through three clear phases. According to practitioners documenting this shift, prompt engineering — focused on message-level instruction design — dominated from 2022 to 2024. Context engineering — focused on architecting what information the model reasons over — became the defining skill of 2025. Harness engineering — designing the full operational environment for autonomous AI agents — is the emerging frontier of 2026 and beyond.
What this means practically for anyone building AI into their work right now is that the skill worth investing in is not finding cleverer ways to phrase a single question. It is learning to design the information environment that surrounds each question — and understanding how to make that environment richer, more relevant, and more consistent over time.
Three Real Examples That Show the Difference
The best way to understand context engineering is to see the contrast between a basic approach and a context-engineered approach on the same task.
Writing a Product Description
The basic approach asks: “Write a product description for a smartwatch.” The output will be generic, applicable to any smartwatch, and sound like it came from a category page on a large retailer’s site.
The context-engineered approach loads the role — a persuasive e-commerce copywriter for budget-conscious Gen Z buyers — alongside specific product details: a smartwatch priced at ₦18,500, compatible with both Android and iOS, focused on health tracking, competing primarily with Oraimo and Xiaomi in the Nigerian market. Then it asks for a product description with a confident, direct call to action. The output speaks to a specific buyer in a specific market about a specific product. It could not have been produced without the context.
Summarising a Document
The basic approach asks: “Summarise this blog post.” The output will be accurate but neutral — a compressed version of what the article said, presented in no particular style for no particular reader.
The context-engineered approach specifies the audience — a busy Nigerian entrepreneur who needs practical information fast — defines a structure of “what it says, why it matters, what to do next,” and sets a tone that feels like advice from a trusted colleague rather than a formal executive summary. The output serves a specific person’s actual need rather than producing a generic compression of the source material.
Generating Content Ideas
The basic approach asks: “Give me YouTube video ideas.” The output will be a list of broadly applicable topics that could apply to any channel in any niche.
The context-engineered approach specifies that the channel produces AI tool tutorials in Nigerian Pidgin, targets three-minute videos, uses relatable local examples, and is optimised for low production complexity. Then it asks for five video ideas with hooks and sample scripts that match those specific parameters. The output is immediately actionable for that specific creator. Without the context, it would have required significant additional work to adapt generic ideas to a specific format and audience.
How to Apply This Starting Today
You do not need to be building enterprise software to benefit from thinking this way. The principles apply whether you are a content creator, a freelancer, a small business owner, or someone using AI to support their daily work.
Start by identifying the three tasks you use AI for most frequently. For each one, write down the context that would make the AI’s output immediately better: the audience, the tone, the goal, the format, and two or three examples of what good output looks like. Save this as a document you can paste at the start of relevant sessions.
If you use Claude, set up a Project for your most important recurring work and upload your context materials there. If you use ChatGPT, populate your custom instructions with your most important context parameters. Both platforms are built to support this approach — most people simply have not taken the twenty minutes required to set it up.
From that point forward, every AI session starts with a model that already knows who you are, what you are building, and what good output looks like in your specific context. The improvement is immediate and it compounds over time as you refine your context based on what is working.
If you want to understand which AI tools are best suited to building these kinds of context-rich workflows, this guide to AI tools that actually deliver results covers the practical options available right now. And if you want to understand how these skills translate into income — what you can offer clients and what the market actually pays for — this honest breakdown of what pays in the current AI economy connects directly to the capability you are building here.
The Honest Nuance Worth Knowing
Prompt engineering is not dead in the sense that prompts no longer matter. They matter. The wording, structure, and clarity of a prompt still affects what comes back.
What has changed is that a well-worded prompt in a poorly constructed context will consistently underperform a simply worded prompt in a rich, well-designed context. The bottleneck has moved. Improving your prompts when your context is weak produces diminishing returns. Improving your context when your prompts are reasonable produces dramatic improvements.
The people who thrive in the AI era are not the ones who found the cleverest phrases. They are the ones who built the best environments — who understood that the model’s output is a reflection of everything it has access to, not just the question it was asked at the end. That understanding, applied consistently, is what separates AI users who get reliable, high-quality results from AI users who keep wondering why the output never quite sounds right.
The next time you sit down to work with AI, ask yourself one question before you type anything: does this model know enough about my context to give me something genuinely useful, or am I about to prompt into a void and hope for the best?
The answer to that question determines the quality of everything that follows.
