Beyond the Buzzwords

Before we build, let's “demystify” some terms I'm required to include in this presentation.

• LLM: Basically a spicy autocomplete that occasionally lies to you.
• RAG: When the AI doesn't know the answer, so it reads a PDF really fast.
• Agentic: Giving the AI a credit card and hoping for the best.
• MCP: A fancy way of saying “the AI can finally see my files.”

“Ask me relevant clarifying questions, one at a time”

— Brett Morgan

The End of Syntax

Engineering is evolving from writing lines of code to orchestrating complex systems through high-level intent.

• The model generates the implementation.
• The engineer provides the architecture and constraints.
• Focus shifts from how to build it, to what to build.

Why Structure Matters

It's not just prompting; it's about building a solid foundation where your AI partner really gets the vision of your project.

• Structure: The overarching rules, patterns and philosophy of a project.
• Intent: Translating high-level goals into functional, deterministic software.
• Reduces cognitive load by offloading syntax memorisation.

What is a PRD?

The Project Requirements Document (PRD) is the absolute Source of Truth for your AI Agent. If it's not in the PRD, it doesn't exist.

A good PRD should contain:

• Executive Summary & Goals
• Strict Technical Constraints (e.g., “No React”)
• Architecture & Spatial Logic
• Success Metrics

Generating the DNA

Don't worry, you don't have to write everything from scratch! You can brainstorm and build your PRD together through a friendly chat with Gemini.

Example Prompt:

Act as a Senior Software Architect.

Create a Product Requirements Document (PRD) for a "Task Manager" web app and save it to `.gemini/prd/PRD.md`.

Ask me relevant clarifying questions, one at a time
                

Once generated, save it to .gemini/prd/PRD.md and lock it.

Agent Instructions

If the PRD is your project's heart, the agent instruction is its personality. It defines how your AI partner thinks, acts, and talks to you.

• Role: “You are a hardcore vanilla JS purist.”
• Action Rules: “Always update index.html via tools, never output full files in chat.”
• Tone: Concise, professional, no fluff.

Setting the Standards

The Agent file is where you enforce creative constraints. It prevents the AI from falling back on generic, "bootstrappy" designs.

By defining clear aesthetic principles like Visionary, Orchestral, Professional, the agent makes implicit micro-decisions about spacing, typography and animation curves without needing a prompt for every CSS property.

Example Prompt:

Create an agent configuration for an AI coding assistant and save it to `.gemini/agents/frontend-specialist.md`.
Please ensure that the markdown file begins with the following frontmatter:
                

What are Skills?

Skills are like handy little playbooks. They teach your AI exactly how to nail a specific task, just the way you like it!

• Stop repeating yourself in prompts.
• Ensure consistency across different projects.
• Create an organizational standard library.

Making a Skill

A good skill document contains strict mandates, code examples, and prohibited patterns.

Example Prompt:

## Mandates
1. Always use semantic HTML.
2. Never use inline styles.
## Prohibited
- Do NOT use React.
                

Remote Skills

Skills don't have to live in your local repo. You can host them remotely and instruct the agent to fetch them.

Example: Fetching https://distribute.kartikarora.me/ai/BRAND-SKILL.1.5.0.md allows a single source of truth for a brand identity across an entire organization's AI tooling.

What are ADRs?

Architectural Decision Records (ADRs) are short text documents that capture a specific architectural decision, its context, and its consequences.

• Context: What problem are we solving?
• Decision: What is the chosen solution?
• Consequences: What are the trade-offs (good and bad)?

ADRs in agent

To ensure your AI agent respects architectural decisions, you can link ADRs directly in your agent instructions or define them as strict rules.

Example Prompt:

## Rules from ADRs
1. ADR-001: Use repository pattern for all data fetching.
2. ADR-004: All styling must use CSS Variables (no hardcoded hex).
                

This makes the why as important as the how for the agent.

What is MCP?

The Model Context Protocol (MCP) is how AI agents interact with the outside world securely.

Instead of just generating text, an agent can use tools to:

• Read local files directly.
• Execute shell commands (safely).
• Query external APIs dynamically.

Real-world Data

With MCP, your structured AI development isn't limited to what the model memorized in training.

You can tell the agent: “Look at my open Github PR, read the feedback, and implement the requested changes in my local branch.”

The context window becomes a dynamic workspace.

Better Context with Context7

Context7 is an open-source MCP server by Upstash that brings the latest documentation and code examples directly to your AI.

The Challenge:

• AI assistants often rely on outdated training data.
• Deprecated APIs lead to hallucinations and broken code.
• Documentation for new library versions is missing.

How it Works

By simply including “use context7” in your prompt, the agent fetches the most current documentation from source repositories.

• Up-to-date Info: Ensures the AI has current API signatures and patterns.
• Reduced Hallucinations: Accurate documentation means less fake or non-functional code.
• Version-Specific: Retrieve documentation for the exact library version you're using.

It covers thousands of popular libraries and frameworks instantly.

Conclusion

AI doesn't replace engineering; it amplifies it. By focusing on structure and intent, you're not just coding—you're architecting at a higher level.

• Architecture over Syntax: Define the system, let the model handle the boilerplate.
• Clear Intent: High-quality prompts lead to deterministic outcomes.
• MCP & Context: Give your agents the real-world data they need.

The Future of Engineering

“Build with Intent. Lead with Structure.”

The real magic of engineering is shifting from memorising syntax to creative problem solving and leading your AI team with a clear vision. You've got this!

Dig Deeper

Keep the momentum going with these great resources:

• Google AI Studio: aistudio.google.com
• Google Skills: github.com/google/skills
• MCP Protocol: modelcontextprotocol.io
• Context7: github.com/upstash/context7

Happy building!