We Never Go Out of Style: Designing Context, Not Just Prompts

Serah Rashidi (She/Her)
Jan 19
6 min read

Hey techies! I hope this year is treating you kindly so far, or at least giving you enough caffeine to survive it. Welcome to the first TechPulse blog of 2026. I’ve honestly been looking forward to writing this one because it feels like a clean slate. A fresh year, new conversations, and a chance to reset what actually matters in tech before we get swept away by yet another wave of buzzwords and hype. And if there’s one thing I want us to do this year, it’s to start intentionally.

Last year, we spent some time talking about prompt engineering. How to ask better questions. How wording, structure, and intent can influence AI outputs. At the time, it felt like the skill everyone needed to have and for good reason. It helped a lot of us learn how to work with AI instead of against it. But let’s be honest for a second, that season has passed.

Today, prompt engineering alone isn’t the conversation anymore. In fact, it’s slowly becoming the bare minimum. The new term you’ll hear everywhere now, in papers, products, and serious AI systems, is Context engineering. And unlike prompts, this one isn’t just about what you type,it’s everything surrounding it. So buckle up because in this blog, we’re starting 2026 in style by tackling Context engineering.

What Is Context Engineering?

So what is context engineering, really? At its core, context engineering is the practice of deliberately shaping everything an AI system sees, knows, and assumes before it produces an output. It goes far beyond the single prompt you type into a chat box.

Context includes the system instructions that define the model’s role, the conversation history it carries forward, the documents or data it can access, the tools it’s allowed to use, the constraints it must follow, and the priorities it is expected to respect. When people say “AI behavior,” what they are usually observing is not just the model’s capability, but the result of all these contextual layers working together (1).

Why Prompt Engineering Alone Isn’t Enough Anymore

This is why prompt engineering alone has started to feel insufficient. A prompt is only one slice of a much larger decision space. Modern language models don’t respond in a vacuum; they generate outputs based on a probability distribution conditioned on the entire context window, not just the last user message (2).

Change the surrounding context, even slightly, and the same prompt can lead to a completely different response. This is also why two applications using the same underlying model can behave in radically different ways. The intelligence you experience is not only embedded in the model weights, but in how the context is constructed and maintained.

Context Engineering for Beginners

As soon as an AI system needs to remember something, retrieve external information, follow policies, or act consistently over time, context stops being optional and becomes architectural.

Retrieval-Augmented Generation, for example, is fundamentally a context-engineering technique. Instead of asking the model to rely on its internal training data alone, relevant documents are retrieved and injected into the context so the model can ground its response in specific sources (3). When done well, this reduces hallucinations and increases factual accuracy. When done poorly, it introduces noise, bias, or irrelevant information that degrades the output.

Another critical aspect of context engineering is instruction hierarchy. Language models are typically influenced by multiple layers of instructions, including system-level directives, developer messages, and user prompts.

These layers are not equal. Higher-priority instructions override lower ones, and ambiguity between them often leads to unpredictable behavior (4). Context engineering involves making these priorities explicit and consistent, so the model does not have to “guess” which instruction matters more. This is one of the reasons well-designed AI systems feel stable and trustworthy, while poorly designed ones feel erratic or contradictory.

Memory design is also part of context engineering. Decisions about what the model should remember, for how long, and in what form directly shape its behavior. Short-term conversational memory helps maintain coherence, while long-term memory can personalize interactions or preserve user preferences across sessions (5).

At the same time, memory introduces responsibility take for example storing sensitive information, it can create alot of privacy risks.

With context engineering comes an important clarification: this doesn’t mean humans are competing with models or that AI is “making decisions for us.” Models can generate text, code, and explanations at scale, but they don’t decide what should be trusted, what constraints are ethically appropriate, or which trade-offs make sense in the real world. Those judgments remain human responsibilities, and they are embedded directly into the context we design. Guardrails, refusal conditions, scope limitations, and safety boundaries all live at this level (6). So when people talk about “responsible AI,” they're often referring to context design at times whether they realize it or not.

For beginners, this shift can feel intimidating, but it doesn’t require building complex agent systems or production-grade pipelines. Understanding context engineering starts with a mindset change. Instead of asking, “How do I phrase this better?”, the more powerful question becomes, “What does this system know, and what am I assuming it knows?”

Even simple choices, like providing clear background information, defining roles explicitly, or limiting the scope of a task, are early forms of context engineering. Over time, these choices compound and shape how reliable and useful an AI system becomes.

Let's Practice with claude.md

So far, context engineering might still feel a little abstract so et’s make it real. One very common pattern you’ll see in real AI systems is something surprisingly simple: a file called claude.md.

In many projects, especially those using models like Claude, teams don’t rely on a single prompt to control behavior. Instead, they define a persistent context file, usually named claude.md, that lives right next to the code. This file isn’t a prompt in the traditional sense. It’s more like a setup phase. Before the model answers anything, it already knows who it is, what it’s here to do, what it should focus on, and what it should avoid and here’s what that looks like in practice.

First, you define who the model is. In claude.md, you might say the model is a technical assistant, a research helper, or a documentation reviewer. This alone changes a lot. Tone stabilizes. Depth becomes more consistent. The model stops guessing what kind of answers it’s supposed to give.

Second, you define scope and rules. You make it clear what the model is allowed to do, what it should refuse, and what matters more when instructions conflict. This is where instruction hierarchy comes in. System rules come first, user requests come after. All of that is decided before a single user prompt ever shows up.

Third, you let that context persist. This is the key difference between prompt engineering and context engineering. The user doesn’t need to repeat expectations every time. The context is always there in the background. The exact same prompt can behave very differently depending on what’s written in claude.md, even though the model itself hasn’t changed at all.

Once you bring the cloud into the picture, this becomes even more powerful. In real deployments, the model usually runs as an API. The claude.md file lives in a repository, gets loaded at runtime, and is combined with other layers of context like retrieved documents, user session state, or tool permissions. The cloud is what assembles all of this dynamically. The model doesn’t know it’s “in the cloud”, it just reasons over whatever context it’s given.

If you’re a beginner, here’s the part that really matters: you don’t need agents, orchestration frameworks, or complex pipelines to start doing context engineering. Even defining a single, well-thought-out context file already changes how the system behaves. The shift is mental. You stop asking, “How do I phrase this prompt better?” and start asking, “What assumptions does this system carry every time it runs?”

Claude.md is just one concrete example, but the idea applies everywhere. Context can live in a markdown file, a database, a retrieval layer, or a cloud service. Wherever it lives, the principle stays the same. You’re not just talking to a model anymore. You’re designing the environment it thinks inside.

So the next time your AI system behaves unexpectedly, remember it's not necessary a problem from the model itself. More often, it’s the environment you designed around it.

Yes, prompts still matter, but they sit on the surface. Context is the system, design it intentionally, and the intelligence you’re working with suddenly starts to make sense.

And with that, we reach the end of the blog. I hope you had a good read and learned a lot. Stay tuned as we'll cover more tech-related topics in future blogs.

In case of any questions or suggestions, feel free to reach out to me via LinkedIn. I'm always open to fruitful discussions.🍏🦜