Context Engineering Explained in 5 Minutes: The Next Wave in AI Coding

You've probably heard about vibe coding, but it turns out that when the term was popularized, it was simply giving a name to something developers had been doing for months. Now, history is repeating itself with a new term: context engineering.

Just like vibe coding, this is not a new invention. Many in the development community have been practicing it for some time. However, its recent popularization highlights a critical point: this is an absolutely necessary approach and the future of how we should code with AI.

This article is more than just an explainer. We will not only go hands-on with what context engineering is and how to prepare the context, but we will also show you how to properly use that context—a step that many developers are completely missing.

From Prompt Engineering to Context Engineering

The first thing to understand is that all AI models have a "context window," which is the amount of text they can remember at any given time. With traditional prompt engineering, we focused on phrasing questions in a specific way to get a single good answer from the LLM.

Context engineering is different. Here, we give the model all relevant facts, rules, tools, and information, effectively filling its context window so there is little to no chance of hallucination. The model knows exactly what it is doing because we are actively shaping what it needs to remember to accomplish our goals.

A helpful way to visualize this is to see context engineering not as a replacement for prompt engineering, but as a broader discipline that encompasses it. It's an umbrella term that includes everything from Retrieval-Augmented Generation (RAG) to memory management, with prompt engineering being just one component within it.

The Rise of Specialized LLM Apps

It's not only the context we need to manage; it's also the application we're using. An LLM app is no longer just a simple wrapper around a chat interface. Modern tools provide workflows and features that are genuinely useful for development.

The LLM app must have the necessary components for effective context engineering. Coding assistants like Cursor and Claude Code are no longer just chat windows; they are crucial components in the context engineering ecosystem. While both have their strengths, the workflow we're about to discuss is adaptable to either, so you can use whichever tool you prefer.

A Practical Workflow for Context Engineering

You might be excited to just dump all your project files into the model's context and expect perfect results. However, it's not that simple. Remember the context window? Once it fills up, the chances of hallucination actually increase rather than decrease.

Efficient management of the context window is crucial. You can't just throw everything into one massive file. You need to break it down into logical pieces and provide them to the model only when needed.

Here is a workflow that has been refined over time, inspired by great ideas from across the developer community, such as including external documentation in the context window.

1. The Project Requirement Document (PRD)

We start with a PRD, where we list the features we want. Based on this, the model can suggest the best path forward. As a developer, you can add specific technical requirements. For example:

• Frontend: Next.js
• Backend: FastAPI

Even if you don't specify a tech stack, this workflow can help the model configure everything and produce a ready-made application.

2. The Documentation Folder

This is the core of the context for the model. It contains four essential files:

• implementation-plan.md: A step-by-step plan for building the entire application.
• project-structure.md: Defines the folder and file layout. (This is often generated by the model based on the plan).
• ui-ux.md: Contains all documentation related to the user interface and experience.
• bug-tracking.md: A log of known bugs and errors to prevent the model from repeating mistakes.

These files provide the different components the AI model needs to complete the project successfully.

3. The Guiding Rules

The model has the context, but it also needs to know how to use it. For that, we set up two simple rules: a generate rule and a work rule.

• The generate rule: This rule takes the PRD and uses it to generate the content for the other documentation files (implementation-plan.md, ui-ux.md, etc.). It builds the complete context for the development process.
• The work rule: This is a smaller, persistent rule that is always attached to the AI assistant. It tells the model how to use the documentation files during development.
  • When implementing a feature, it refers to the implementation-plan.md.
  • When working on the UI, it consults the ui-ux.md file.
  • Before creating a file or running a command, it checks project-structure.md for consistency.
  • If an error occurs, it first checks bug-tracking.md.

This workflow rule is kept intentionally small to take up as little space as possible in the model's active context window.

The Importance of Careful Review

A critical point to remember is that AI models follow instructions blindly. You must be very careful and read everything you give them.

For instance, in one scenario, a PRD specified building an MVP, but the generate rule contained instructions for a full application build-out with advanced features. The model followed the more detailed instructions in the generate rule, ignoring the MVP scope.

Note: Never blindly accept a file, configuration, or any other output from an AI model. Always read through it carefully and adjust it to your specific needs. Taking an hour to review the generated plan can save you days of headaches later.

Putting the Workflow into Action

When you start a new chat with your AI assistant, it won't know anything about the project. But by providing the implementation-plan.md, you give it all the context it needs.

The assistant will then: 1. Read the implementation plan. 2. Create a to-do list based on the tasks outlined in the plan. 3. Execute each task step-by-step, installing dependencies, creating folders, and setting up the project structure.

The model knows exactly what it's doing because the context is right there, and it's following the instructions sequentially. This methodical process ensures that the project's foundation is built correctly. In software development, you can't just implement features randomly. If the foundational structure isn't solid, you'll either restart from scratch or face a mountain of technical debt.

Final Thoughts: Build Your Own Workflow

While you can find templates for these documentation files, the most important takeaway is to understand the principles of context engineering. With that understanding, you can build your own implementation plans, generation workflows, and the exact set of files that your preferred AI assistant needs to succeed.

This approach allows for better context management than dumping everything into a single file. By breaking down the project into logical, manageable pieces, you guide the AI toward a more accurate and reliable outcome, truly harnessing its power for complex development tasks.