Context Engineering: A Revolução do Vibe Coding | Tutorial Completo 100x Mais Eficiente

## From Vibe Coding to Context Engineering

This video from WorldofAI introduces "Context Engineering" as a significant advancement over "vibe coding" in AI-assisted software development, emphasizing its effectiveness in generating reliable code (0:00). Vibe coding, a term coined by Andrej Karpathy, involved quickly prototyping applications using natural language for AI to generate code (0:00). However, Karpathy and Shopify CEO Toby now advocate for context engineering, which focuses on providing all necessary information to a large language model (LLM) to plausibly solve a task (0:19, 0:37). This approach is not about clever prompts, but about carefully curating precise information through a blend of science, intuition, and system design, powering real-world AI applications beyond simple ChatGPT wrappers (0:47). Context engineering is becoming a core skill for AI-assisted development, similar to how vibe coding revolutionized prototyping (1:11).

## Why Context Engineering is Crucial for AI Code Quality

Context engineering addresses the significant challenge of AI trust and accuracy in code generation (1:28). A study by Codel revealed that 76.4% of developers distrust AI-generated code without human review, primarily due to frequent hallucinations and mistakes (1:28, 1:48). The problem isn't AI coding itself, but the lack of human oversight and, critically, the AI's frequent lack of or missed context (1:53, 2:01). WorldofAI highlights tools like Taskmaster and Context 7 on their channel that provide AI with necessary context for correct code generation (2:11). Context engineering is vital because it structures and feeds the right information to the AI, enabling it to successfully complete generation tasks (2:23).

Context engineering is defined as the skill of carefully selecting, organizing, and managing the right information an AI or AI agent needs at each step to perform a task efficiently and effectively, preventing the AI from being overwhelmed or missing critical details (3:09). Context encompasses everything the model "sees" before generating an output, including the project's state, conversation history, user prompt, available tools, Retrieval-Augmented Generation (RAG) instructions, and long-term memory (3:38). Full context allows the AI to reference all these elements in a single area to produce the best structured output, achieving the right balance by feeding the AI necessary, useful, and structured information step-by-step at the right time (3:53, 4:04).

## Implementing Context Engineering with a Dedicated Template

The video introduces a context engineering template developed by Cole Mission, designed to significantly improve code generation (4:14). This template works with any AI coding assistant but is specifically built to leverage Claude's strengths, aiming for more flexible and precise generation, fewer tokens, and better overall output (4:24, 4:36).

To use the template, users need Git to clone the repository, Node.js for Claude Code's functionality, and Claude Code itself, which can be easily installed (4:47).

The setup process involves several steps:
1. Clone the GitHub repository for the template into your command prompt (5:08).
2. Open the repository in your Integrated Development Environment (IDE) for easy preview and configuration (5:22).
3. Configure the `cloud.md` file, which sets global rules for Claude, defining project-wide parameters like code structure, testing, reliability, task completion, system, and conversation parameters (5:51). Users can customize these based on their preferences (6:07).
4. Create an initial feature request in `initial.md` (6:26). This file describes the features to be built, including a clear description of the AI's focus in the Features tab, example files for AI reference in the Example tab, links to relevant documentation or APIs, and additional considerations for edge cases or specific requirements (6:35).

## Generating and Executing a Product Requirements Plan (PRP)

A crucial part of context engineering is generating a Product Requirements Plan (PRP), which acts as a detailed product requirements document guiding the AI coding assistant (08:24). The Context Engineering intro template includes pre-set slash commands in a `commands` folder to help generate and execute PRPs, managing arguments and variables (08:43).

To begin, users open Claude Cloud Code in their terminal, set the mode, and configure their API key for login (09:11). Once authenticated, the `generate PRP` command is run based on the `initial MD` requirements (09:22). The generation process takes time as the AI thoroughly drafts the PRP, referencing all provided information and incorporating requirements using a specific template (09:39, 09:47).

The AI's ability to plan thoroughly, research APIs, check the codebase, review examples, and read documentation provided in `initial MD` is a significant benefit (10:13). This detailed planning addresses major issues with AI coding assistants, such as hallucinating, making incorrect API calls, or missing critical details (10:35). Context engineering enables the AI to perform real research and build a reliable plan before writing any code, ensuring confidence in the generated output (10:49). Once developed, the PRP is stored in the `PRPs` folder (11:02). An example PRP for a "multi-agent research email system" showed documentation, referenced files, links, the current codebase tree, and the desired codebase tree with new files (11:14). This approach significantly reduces hallucination and saves on token output, streamlining the entire code generation process (11:35, 11:46).

After generating the PRP, the next step is to execute it, allowing the AI to code the application (11:57). The `execute PRP` command is used, specifying the folder and filename of the PRP (11:57). This command initiates the application generation, which can take time and consume a significant number of tokens (12:18). The AI starts by creating a to-do list: setting up the project directory, defining requirements, implementing the agent, and running validation commands to fix issues (12:31). This in-depth process demonstrates how the AI systematically creates the application, tackling each task sequentially and referencing the detailed to-do list and PRP to produce high-quality output (12:42, 13:09). Unlike single-shot prompts, context engineering pulls all relevant content into one area and feeds it to the LLM for comprehensive processing, leading to superior results (13:27).

After implementation, the setup steps include copying and configuring API keys in environments, installing dependencies, running the `Python main.py` file, and testing with `pytest` (13:57). The AI agent is fully created and constructed based on the PRP, costing $3.32 for the example shown (14:21).

## Testing the AI Agent and Concluding Remarks

The video concludes by demonstrating the functionality of the newly created AI agent (14:34). The multi-agent research and email system, created via a single PRP, can research using the Brave API and draft emails via Gmail (14:34). The multi-agent system seamlessly delegates tasks between different agents (14:54). A test command, "create me an in-depth research on the world of AI," was processed by the system (15:02). The AI used the Brave search tool and research agent to compile results, fully constructing the research without manual intervention, quickly and affordably, thanks to context engineering's ability to thoroughly reference information (15:15, 15:28). The output included linked sources and detailed research on "the world of AI" (15:42).

Context engineering elevates the coding experience by streamlining the code generation process (16:12). WorldofAI encourages viewers to follow them on Twitter, subscribe to the channel, turn on notifications, and like the video (16:44). They also recommend exploring their previous videos for beneficial content (16:48). The speaker expresses gratitude to the audience for watching, wishes everyone an amazing day, and encourages spreading positivity before signing off (16:53).

This summary was generated with YT Video Summarizer + Note Taker Chrome Extension

This is a good guide just now toss cursor aside and use Genspark AI devoloper for this and you hot next level stuff I would look into that

شكرا👍👏👍👏🤍🤍

I'm still very confused, could you please do a simple to-do app

This is minimum of 10K token. That is why I refrain using PRP workflow. Your $5 will be gone in just 2 prompt. First you generate the PRP that's 3k to 5k token combine input/output token. Then execute it will be around of 5k to 8K combine input/output token. AI always hallucinate when the prompt is to large. The best prompt are less than 300 tokens. Overall this is good if you just run this in few times. Not recommended if you are building your own Saas based AI product.

Context.ai: NEW Super AI Agent! INSANELY Powerful AI Agent Can Automate and Build Anything!: https://youtu.be/e-ixBImw3BA

Isn't context engineering just building Agentic AI or LLM-based applications? Why name it differently then?

One person says Context Engineering is Hype, the other says it offers Hope. This is how an industry is built…
What we need is an evaluator model. Like a GAN setup. Give you more confidence to let it do it's thing for the next 90 minutes or so.

is there templates for other code agents, other than claude?

The term engineering used quite loosely here, as in: context engineering is to real engineering what doctor phil is to real doctors.

This is what happens when computer "scientists" pretend to be engineers.

i build a massive text based web game doing this instead of just throwing out stupid prompts lots of .mds for showing how and where code connections happen + rules+ mpc context 7+ a few other things

Vibe coding was just a comment taken to its logical extreme. Context engineering is a principle.

Always follow principles over extrapolating commentary.

Questions: Can you use a different example of Context instead of using the repo included example Context. I would like to see how this framework can be generalized and it seems like only INITIAL.md should be modified before Claude Code can take action. Thanks for sharing.

Great video! The comparison between vibe coding and context engineering really hit home. I've definitely been guilty of vibe coding in the past. 😅 Excited to try Claude Code with a more structured approach now. Anyone else find context engineering makes debugging easier?