Context engineering intro: Why prompts alone aren't enough anymore

Not long ago, we were all caught up in "vibe coding". You'd open ChatGPT, type "Build me a to-do app", and watch code appear like magic. No setup, no planning, just vibes.

It was fun, but as projects got bigger, we hit limits. The AI could guess what we wanted, but not always why or how. That's where context engineering comes in, the natural next step after prompt engineering.

Instead of just crafting good prompts, context engineering is about giving the model the full picture: the rules, data, tools, memory, and structure it needs to reason instead of guess.

In this blog, we'll talk about what context engineering really is, how it evolved from vibe coding, what makes good context, and how you can use it to build more reliable AI systems.

Context engineering introduction

At its core, it's about designing the environment around an AI model so it can think clearly instead of guessing.

Every time you ask an LLM to do something like write code, summarize notes, or plan a project, it only knows what you send right now. It doesn't remember who you are or what you said before. Context engineering fills that gap by giving it everything it needs: the rules, data, memory, tools, and structure to reason through a task.

Think of it as setting the stage before the show.

• Prompt engineering is telling the actor what line to say.
• Context engineering is giving them the full script, the backstory, and the lighting cues.

In practice, it means deciding:

• What information to include (files, docs, recent messages, or database results)
• What rules to enforce (tone, format, goals, or constraints)
• What tools to connect (APIs, search, calculators, schedulers)
• How to structure it all so the model can work step by step

The goal is to build systems, AI agents, assistants, and coding copilots that can consistently deliver accurate, context-aware results.

And this especially matters in vibe coding apps, where you're building with AI in real time. Those "build me this" moments only work when the model actually has the right context. Context engineering makes sure it does.

Context engineering vs prompt engineering

Prompt engineering and context engineering often get mentioned together, but they solve different problems. Here's how they differ in practice:

Context engineering in Practice

Let's say you ask an AI to build a login system.

You type: "Create a login page for my website".

It gives you working code, a form, a few input fields, and maybe a fake authentication check. It looks fine at first, but it's generic. It doesn't connect to your database, it doesn't match your tech stack, and it probably skips basic security checks.

That's where context engineering changes everything.

Instead of sending a single prompt, you set up the environment the model needs to think clearly:

• Role: "You're a full-stack developer building a secure login page using React and Appwrite for authentication."
• Rules: "Use Appwrite's built-in auth API, validate inputs, and return proper error messages."
• Files: Include your existing App.js file so it understands where this login component fits.
• Tools: Allow the model to read Appwrite's API docs or schema.
• Memory: Keep the earlier chat so follow-ups like "add password reset" still make sense.

This is just the starting point, but you get the idea, right? You're not asking the AI to guess anymore. You're giving it enough context to reason, follow your setup, and build something that actually fits.

Challenges with context engineering (and how to fix them)

Context engineering sounds great in theory, but in practice, it comes with its own set of problems. The good news? Most of them have simple fixes.

Here are some of the most common challenges:

1. Too much information, not enough space

LLMs can only process a limited amount of input (the context window). Stuffing every file, note, and instruction into one request usually makes things worse.

Fix: Summarize or compress old information. Keep only what's relevant to the current step. Think of it like managing RAM, load what you need, and archive the rest.

2. Unstructured inputs

Dumping raw text or multiple files without clear boundaries can confuse the model. It can't tell what the background is and what the actual task is.

Fix: Use structure. Add headers, delimiters, or short descriptions like: "Below is the project brief.", "Next is the code snippet." Even simple labels help the model separate information correctly.

3. Conflicting sources

When you connect multiple tools or databases, the model might not know which one to trust.

Fix: Set rules for priority. For example: “Prefer live API data over cached results,” or “Follow design guidelines from style_guide.md when in doubt.”

4. Memory overload

As systems grow, stored memory can become messy, and the model starts pulling in outdated or irrelevant details.

Fix: Organize memory in blocks (for facts, tasks, user details, etc.) and regularly clear or refresh what's no longer needed.

5. Context drift

Long conversations or chained tasks can slowly shift the model off course; it starts forgetting tone, rules, or formatting.

Fix: Reassert the essentials. Include a short reminder in every new request, like, “Remember: use Appwrite auth, React, and JSON format.” Small nudges keep consistency.

Frequently asked questions (FAQs)

1. What is context engineering in simple terms?

Context engineering is the process of designing everything around an AI model: rules, data, tools, memory, and structure, so it can reason instead of guessing. Instead of just prompting the model once, you create an environment where it has enough information to consistently make accurate decisions.

2. How is context engineering different from prompt engineering?

Prompt engineering focuses on crafting good instructions, getting one accurate or creative response from a model.

Context engineering goes a level deeper: it's about building the entire setup that the model works within. You decide what inputs it sees, what rules it follows, what tools it can use, and how information flows between steps. Prompt engineering is the message; context engineering is the system.

3. Why does context matter so much for coding with AI?

Because AI tools can't infer your full setup unless you tell them, in coding workflows, context refers to providing the model with access to your project files, API documentation, frameworks, or design choices. Without that, it can't produce code that actually fits your stack. It'll just generate something generic.

4. What are common mistakes to avoid in context engineering?

The biggest mistakes are overloading the model with too much data, mixing unstructured inputs, and forgetting to reassert key rules as a conversation or workflow evolves. Small habits like labelling inputs or pruning old memory drastically improve consistency.

Wrapping up

Prompt engineering works well for simple, one-off tasks: asking questions, drafting text, or generating quick snippets of code. But as soon as you need consistency, structure, or outputs that hold up across steps, you have to think beyond just the prompt.

Good results come from giving AI more than instructions. It needs clarity, data, and structure. The better you set things up, the better the model performs.

More resources

• Comparing the best vibe coding tools
• 20 vibe coding security best practices
• Choosing the right AI database for your application in 2025