Context Engineering Explained in 5 Minutes: The Future of AI-Powered Development |

You've likely heard of vibe coding, a term for a practice developers had been using for months before it was officially named. History is repeating itself with a new, essential concept in AI-assisted development: context engineering.

While the term is fresh, the practice isn't. Many forward-thinking developers have already adopted this methodology. The key takeaway is that this approach is absolutely necessary for effectively coding with AI. This article is more than just an explainer; we will go hands-on with what context engineering is, how to prepare the necessary context, and, most importantly, how to properly use that context—a step many developers are completely missing.

From Prompt Engineering to Context Engineering

First, it's crucial to understand that all language models operate within a "context window," which is the amount of text they can remember at any given time. With traditional prompt engineering, we carefully phrase prompts to get a single, high-quality answer from the LLM.

Context engineering is a broader, more robust approach. Instead of a single prompt, we provide the model with a comprehensive set of all relevant facts, rules, tools, and information. By filling the model's context window with this rich environment, we drastically reduce the chance of hallucination and ensure the model has everything it needs to perform the task accurately. We are actively shaping what the model needs to remember to accomplish our goals.

This evolution signifies a shift from simple prompt engineering to the more holistic practice of context engineering. It's an umbrella term that encompasses various techniques, including Retrieval-Augmented Generation (RAG), memory management, and prompt engineering itself.

The Critical Role of the LLM App

The application you use is just as important as the context you provide. A modern LLM app is no longer a simple wrapper around a chat interface. It's a sophisticated tool with integrated workflows designed for complex development tasks.

Apps like Cursor and Claude Code are prime examples. They are not just chat windows; they are essential components in the context engineering ecosystem, providing the features necessary to manage and utilize context effectively. Both have their own strengths, and while one may seem more powerful at any given moment, they are both rapidly evolving. The workflow described here is adaptable and works effectively in either application, so you can use whichever you prefer.

A Practical Workflow for Context Engineering

Now that you understand the concept, you might be tempted to just feed the model every piece of information you have. However, this is where a strategic approach becomes vital.

Remember the context window? Once it's full, the model's performance can degrade, and the likelihood of hallucinations actually increases. Efficient management of the context window is paramount. You can't just dump everything into one massive file. The key is to break down information into logical pieces and provide them to the model only when needed.

Here is a proven workflow for context engineering. It’s a system refined over time, recently updated with the clever idea of including external documentation directly in the context window.

Step 1: The Project Requirement Document (PRD)

We begin with a PRD, where we list the desired features for the project. Based on this document, the model can make informed decisions. As a developer, you can add specific technical requirements. For instance, you might specify:

Frontend: Next.js
Backend: FastAPI

Even if you're unsure about the tech stack, this workflow can automatically configure the entire setup and produce a ready-to-use application.

Step 2: The Documentation Folder

This is the core of the context for the model. It contains several key files that the AI needs to complete the project:

Implementation Plan: A step-by-step guide for building the application.
Project Structure: A file (initially empty) that gets populated as the project is built, ensuring consistency.
UI/UX Documentation: Guidelines for the user interface and experience.
Bug Tracking: A log of known issues to prevent redundant work.

The Rules of Engagement: Generate and Work

The model needs to know not only what the context is but also how to use it. This is managed by two simple rules.

The Generate Rule: This rule takes the PRD and uses it to generate the content for all the other documentation files. It builds the complete context required for the development process. Once this is done, the model's context for that session is full, and it's time to move on to the next phase to maintain quality.
The Work Rule: After the context is generated, we switch to an implementation-focused approach. The Work Rule is a small, efficient file that is always attached to the LLM app's context. It tells the model exactly how to use the documentation files:
- When building a feature, it refers to the Implementation Plan.
- When working on the UI, it consults the UI/UX Documentation.
- Before creating a file or running a command, it checks the Project Structure for consistency.
- If an error occurs, it first checks the Bug Tracking file.

This rule is kept intentionally small to occupy minimal space in the context window, leaving more room for task-specific information.

The Perils of Blind Trust

A critical lesson in context engineering is that you must be meticulous. AI models follow instructions blindly. In one instance, a request for an MVP (Minimum Viable Product) implementation plan was escalated into a full-scale application plan. Why? Because the generate prompt contained instructions to build the entire application in stages, which contradicted the MVP request. The model followed the more detailed instructions.

Note: Always read everything you give to an AI model. If there are conflicts or contradictions, the outcome can be unpredictable. Never blindly accept a file, configuration, or code snippet generated by an AI. Take the time to review and adjust everything to fit your workflow. This initial investment will save you from significant problems down the line.

A Note on Tech Stack

While this workflow can be fully automated, it's highly recommended that you decide on the tech stack yourself. The AI might choose a stack that is technically compatible with the PRD but not with your own skills or resources (e.g., integrating a service you don't have access to). Researching and defining the tech stack yourself is a small manual step that ensures the project remains aligned with your capabilities.

Putting It All Into Practice

With the context established, you can start a completely new chat with the LLM app and ask it to begin building. Even with a fresh chat and an empty context window, the model can immediately get to work by referencing the Implementation Plan. It knows what to build, what the tech stack is, and how to proceed.

The model will create a to-do list based on the plan and execute each step methodically. This step-by-step process, now a feature in tools like Cursor, ensures that the model stays on track and verifies each action before moving on. You can watch as the project structure—backend, frontend, scripts, and shared folders—takes shape from the ground up.

The foundation is laid correctly because the model has all the necessary information. In software development, building on a solid foundation is non-negotiable. Without it, you'll face endless refactoring and scalability issues. This methodical, context-driven approach prevents that.

The ultimate goal is for you to understand the principles of context engineering. With this understanding, you can build your own custom workflows, generation rules, and documentation sets tailored to your specific needs, whether you're using Cursor, Claude Code, or any other advanced LLM application.