New blog post

Author: Apoo711

src/blog/3d-graph-plugin.md

@@ -5,7 +5,7 @@ tags:
   - post
 layout: layouts/post.njk
 excerpt: "A deep dive into the Obsidian 3D Graph plugin. Discover its core features and take a behind-the-scenes look at the technical challenges and solutions involved in its development, from solving complex CSS color issues to building a real-time settings UI."
-descri[tion: "A deep dive into the Obsidian 3D Graph plugin. Discover its key features, how it was built, and how it can help you find new connections in your notes."
+description: "A deep dive into the Obsidian 3D Graph plugin. Discover its key features, how it was built, and how it can help you find new connections in your notes."
 permalink: "/blog/{{ page.date.getFullYear() }}/{{ page.fileSlug }}/"
 sitemap:
    priority: 0.5

src/blog/context-engineering.md

@@ -0,0 +1,87 @@
+---
+title: "Beyond the Prompt: A Practical Guide to Context Engineering with Gemini"
+date: 2025-07-07
+tags:
+  - post
+layout: layouts/post.njk
+excerpt: "A practical guide to Context Engineering with Google Gemini. Discover how to move beyond simple prompts and use a structured framework to provide comprehensive context to your AI, resulting in more reliable and consistent code."
+description: "Tired of AI-generated code that's almost right? Discover Context Engineering, a new way to work with Google Gemini. This post introduces a framework that helps you elevate your AI from a simple tool to a true development partner."
+permalink: "/blog/{{ page.date.getFullYear() }}/{{ page.fileSlug }}/"
+sitemap:
+  priority: 0.5
+---
+
+## Introduction: The "Good Guess" Problem
+
+We've all been there. You're working on a project, and you ask an AI assistant like Gemini for a code snippet. You ask it to "create a function to handle user uploads," and it gives you a perfectly functional, textbook-quality piece of code. But it's not *your* code. It doesn't use your project's custom error handling, it doesn't know about your specific database client, and it certainly doesn't follow your team's unique style conventions.
+
+The AI made a "good guess," but it lacked the one thing a human developer on your team has intrinsically: **context**.
+
+This is the core challenge of AI-assisted development. These powerful models are incredible at general problem-solving, but they operate in a vacuum, unaware of the specific ecosystem of your project. So, how do we bridge that gap? How do we move beyond just "good guesses" and get consistently reliable, production-ready code from our AI partner?
+
+## Prompt Engineering vs. Context Engineering: An Evolution
+
+For a while, the answer seemed to be **Prompt Engineering**. This is the art of crafting the perfect, clever question. It's like giving a world-class chef a very specific, detailed order for a single dish. It's powerful, but it's limited to that one request. If you want a different dish, you have to write a whole new order.
+
+But what if, instead of just giving the chef an order, you prepared the entire kitchen for them? What if you provided them with your pantry's inventory, your family's dietary restrictions, and your favorite recipes? Suddenly, the chef can do more than just follow one order; they can cook *for you*.
+
+That is **Context Engineering**.
+
+It's the discipline of systematically providing an AI with the full environment—the project rules, the existing code patterns, the required documentation, and the validation steps. It's the difference between giving the AI a sticky note and handing it the full screenplay.
+
+## Introducing the Gemini Context Engineering Framework
+
+To make this discipline practical and repeatable, I created the **Gemini Context Engineering Framework**, an open-source project designed to solve this exact problem.
+
+Its mission is simple: to provide a structured, repeatable workflow for building software with Gemini, elevating it from a simple tool to a true development partner.
+
+![Blueprint and Gem Icon](/images/context-engineering-icon.png)
+
+## How It Works: The Two-Step Workflow
+
+The framework's power lies in its simple but effective two-step process, which separates planning from implementation.
+
+**Step 1: The Blueprint (Generation)**
+First, you don't just ask the AI to write code. You start by running the `generate-prp.sh` script with a simple feature request. This script asks Gemini to act as a senior engineer and create a detailed technical plan, or a **Product Requirements Prompt (PRP)**. This plan includes a proposed file structure, a task breakdown, and even a validation plan. The AI is forced to think through the entire problem *before* writing a single line of code.
+
+**Step 2: The Build (Execution)**
+Once you have this detailed blueprint, you kick off the `execute-prp.sh` script. This takes the comprehensive PRP and gives it back to Gemini with a clear instruction: "Build this." Because all the research, planning, and context gathering is already done, the AI can focus entirely on executing the plan and writing clean, correct code that fits perfectly into your project.
+
+This separation of planning from implementation is the key. It prevents the AI from making "good guesses" and ensures it follows a pre-approved, context-aware plan every time.
+
+## Why This is a Game-Changer for Gemini Users
+
+Models like Gemini are incredibly powerful and can process vast amounts of context. This framework is designed to leverage that specific strength.
+
+By providing structured context—the project rules in `GEMINI.md`, code examples in the `examples/` folder, and a detailed plan in the PRP—you are effectively guiding Gemini's powerful reasoning capabilities. The result is code that is not just functional, but also consistent with your project's architecture, style, and conventions.
+
+This workflow dramatically reduces "prompt fatigue" and the frustrating cycle of generating, correcting, and re-generating code. You get it right the first time, more often.
+
+## Getting Started in 5 Minutes
+
+The best way to understand the power of this workflow is to try it yourself.
+
+You can find the complete open-source project on GitHub.
+
+[Source code](https://github.com/Apoo711/context-engineering-gemini)
+
+Getting started is simple:
+```bash
+# 1. Clone the repository
+git clone https://github.com/Apoo711/context-engineering-gemini
+
+# 2. Define your feature in INITIAL.md
+# ...
+
+# 3. Generate the plan
+./.gemini/scripts/generate-prp.sh INITIAL.md
+
+# 4. Execute the plan
+./.gemini/scripts/execute-prp.sh PRPs/initial_prp.md
+```
+
+## Conclusion: Building with an AI Partner, Not Just a Tool
+
+Context Engineering is more than just a new way to prompt. It's a fundamental shift in how we collaborate with AI. By treating our AI assistant not as a magic black box, but as a skilled partner that needs proper briefing, we can unlock a new level of productivity and code quality.
+
+This framework is a starting point. I invite you to explore the repository, try the workflow on your own projects, and see the difference for yourself. Stop settling for "good guesses" and start building with a true AI partner.