Context Engineering: The Skill Quietly Replacing Prompt Engineering
- Natasha Tatta
- 5 days ago
- 7 min read
For the past three years, everyone has been obsessed with prompt engineering.
Crafting the “perfect prompt,” long or short, expert-level or magic, as if they were secret cheat codes.
But if you’ve been following the evolution of generative AI models, one thing became clear by late 2023:
Prompt engineering was destined to fade. As models grew more capable of reasoning, rigid formulas became obsolete, and the real advantage shifted to something deeper: context engineering.
Teams at OpenAI, Anthropic, and Google barely worry about “good prompts” anymore, since they use something far more powerful: context stacks.
Let’s take a look at why context engineering is becoming the next strategic skill, and how you can start applying it today when working with chatbots.
What Is Context Engineering?
If a prompt tells a generative AI model, or more specifically, an agent, what to do, the context tells it how to think.
Context engineering is the practice of designing the environment in which the agent will operate before it generates anything.
In other words, you define:
Who the agent should “be” (persona, role, expertise).
What it’s trying to accomplish (goals, intent).
How it should communicate (tone, style, structure).
What it should rely on (examples, data, rules, previous work).
With strong context engineering, the agent stops behaving like a simple tool…and starts acting like a trained virtual assistant.
This is how superusers consistently get better results than casual users: it’s not because they write “better prompts,” it’s because they create better briefs.
Prompt engineering is like giving an employee a single instruction.
Context engineering is like giving them training, a role, a manual, examples, boundaries… and then an instruction.
Put simply, a prompt depends on wording, but context depends on understanding.
The difference with an example
Classic prompt:
“Write a LinkedIn post about AI productivity tools.”
The result is a hit-or-miss. Sometimes helpful, sometimes very generic.
Context-engineered version:
“You are a tech founder known for practical, viral insights, with a confident and slightly provocative tone rooted in real use cases. Here are three sample posts as reference. Your audience: CEOs, freelancers, and entrepreneurs. Write a new post about AI productivity tools in this style, taking all this information into account.”
See the difference? It’s no longer a prompt. It’s a brief.
You’re giving the agent an identity, a purpose, a framework, and direction—just as you would with a human employee.
Prompt engineering is talking to the agent, interacting with it; context engineering is training the agent before you even start the conversation.
Why context becomes even more crucial as AI models scale
The new generation of generative AI models like GPT-5, Claude 4.5, Gemini 3 and others are reasoning models. They follow multi-step logic, interpret documents, and handle complex tasks… but only if they understand the context they’re operating in.
This is why prompt engineering is losing relevance.
No more esoteric formulas. No need to repeat “act as…” with every prompt. No endless 500-word monologues just to get a decent answer.
But you absolutely need context. As one Anthropic engineer put it:
“Good outputs come from good instructions. Great outputs come from great context.”
And research backs this up: the study referenced below shows that future model performance will depend far less on how prompts are written… and far more on the quality, structure, and relevance of the context provided.
By contrast, context engineering brings together multiple elements such as role, data, rules, examples, and memory, to create a system that’s far more stable, flexible, and coherent, where the prompt itself becomes only a small part of the work.
A simple way to start with context engineering: the 4Cs framework
Here’s a model you can reuse to structure your interactions with AI, no matter the task or the type of agent or chatbot you’re working with:
Character – Who is speaking?
A product manager, a marketer, a teacher, a designer, a CEO?
Command – What should the agent do? Analyze, create, rewrite, summarize, plan…
Constraints – What rules must it follow? Tone, length, structure, restrictions, target audience…
Context – What does the agent need to succeed? Examples, data, guidelines, objectives, prior work…
Another 4C framework, proposed by Status Neo, offers a slightly different perspective:
Clarity – crafting clear, unambiguous instructions.
Continuity – maintaining context across interactions.
Compression – summarizing long content efficiently.
Customization – adapting the context to user roles and needs.
Over time, the agent begins to think like you, your judgment, your priorities, your style. And that changes EVERYTHING.
Conversation → Project → Custom GPT or dedicated agent
These frameworks become especially powerful once you move beyond simple one-off conversations.
For example, ChatGPT Projects or Claude Artifacts already allow you to retain some context across sessions, but it remains partial, limited, and usually tied to a specific task or scope.
With a dedicated agent such as a custom GPT, the context becomes truly persistent, structured, and reusable: role, rules, style, data, memory, tools. The agent knows what it’s supposed to do, regardless of the prompt.
The difference is gradual:
Regular conversation: minimal and temporary context.
Project or artifact: context retained but narrow and task-focused.
Dedicated agent (e.g., a custom GPT): full, durable, orchestrated context.
The more structured and persistent the context, the more coherent, accurate, and reliable the results become.
Why context engineering matters more than prompt engineering
Here’s a truth most people don’t say out loud:
Prompt engineering relies a little on luck, while context engineering relies on structure.
Without context, you’re rolling the dice.
With context, you’re building a system. Let’s look at another example.
Prompt-only version:
“Analyze our customer support tickets from Q4 2025 and summarize the main issues.”
Context-engineered version:
“Here are our customer support ticket logs (CSV attached). Time period: October to December 2025. Intended audience: the executive team, evaluating operational inefficiencies. Goal: reduce ticket volume next quarter. Expected output: bullet-point insights plus recommended actions for leadership. Analyze the dataset using this context.
One is asking for a miracle. The other provides the ingredients.
The role of the context window
Today's AI models can process massive amounts of information:
GPT-5.1: up to 400K tokens
Claude Opus 4.5: 200K
Gemini 3 Pro: 1 million
This means you can upload things like:
brand guidelines,
a style guide,
sample work,
datasets,
full reports,
code,
product documentation, specs, and more.
But bigger isn’t always better: the larger the window, the more you pay… and the more noise you introduce. That’s where context engineering becomes essential.
❌ It’s not about giving more information.
✅ It’s about giving the right information.
Best practices for context engineering
Design clean, specific tasks Clear goals, defined audience, expected format.
Define a persona This heavily shapes the agent’s reasoning and tone.
Provide examples AI models learn by analogy: show, don’t just tell.
Upload the data it needs AI can’t use information it doesn’t have. Well… except when it hallucinates.
Connect the AI to the right information Report excerpts, internal procedures, FAQs, spreadsheets, and so on.
Validate step by step Break complex tasks down into smaller, manageable steps.
Why context engineering matters for the future of work
Across every field, from marketing to translation, education, design, consulting, production, and beyond, the people who thrive aren’t the ones writing the “best prompts.” In fact, the perfect prompt doesn’t even exist.
The ones who excel are those who:
design the environment,
structure the information,
control the data and inputs,
guide the reasoning.
In other words, the ones who master context engineering.
And even beginners can produce work that feels like it came from an entire team.
More than a skill, context engineering is a strategic advantage.
A word about RAG systems (Retrieval-Augmented Generation)
To go even further, RAG systems allow the AI to fetch the right information from the right source, like your documents, your data, your internal resources, before generating a response. This leads to fewer errors, higher accuracy, and an AI agent that relies on a real knowledge base rather than guesswork.
“For organizations, the path is straightforward: turn their internal document assets into a strategic advantage through a RAG system. This involves four key steps: start by auditing internal document repositories, launch a pilot project within a specific department, measure the actual gains, then scale the approach across the organization.”
Héon, Michel. (2025). Comprendre le RAG – Vers une IA qui interroge vos documents intelligemment.
No, context engineering is not just an upgraded megaprompt
It’s normal to confuse context engineering with a “megaprompt”: a long, highly detailed instruction, especially when so much advice online tells you to pack your prompt with roles, examples, constraints, and goals.
In reality, a megaprompt is still a one-off instruction: everything is sent in a single message and forgotten as soon as you move on to the next prompt.
Context engineering doesn’t try to make the prompt heavier. Instead, it builds a persistent framework: a role, a style, rules, data, memory, or a RAG system that the agent can reuse automatically.
A megaprompt improves a single instruction.
Context engineering improves the entire system around the instruction.
Which is why you can then use shorter prompts and still get consistent, aligned, high-quality results.
The next step: building your own context system
If you want an edge going into 2026, remember this:
What matters isn’t what you ask a chatbot, it’s what it already understands before you ask anything at all. Make sure you:
build the right environment,
define expectations,
structure the information, and
refine things as the conversation evolves.
Stop hacking your prompts. Start building your context.
Want to kickoff 2026 on the right foot with a solid understanding of Gen AI, the tools, and best practices?
📅 I’m hosting an introductory webinar on Gen AI on December 18 for beginners.
For priority access to tickets, sign up for the newsletter. (Spots will be limited.)
Natasha Tatta, C. Tr., trad. a., réd. a. A bilingual language specialist, I pair word accuracy with impactful ideas. Infopreneur and Gen AI consultant, I help professionals embrace generative AI and content marketing. I also teaches IT translation at Université de Montréal.
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