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AI is no longer an experiment in software development.
It’s writing production code. It’s refactoring applications. It’s generating tests, scaffolding architectures, and—depending on the workflow—taking action across entire repositories. For many developers, AI is already embedded in daily work.
And yet, most teams are still using it without a plan.
They rely on prompts that “seem to work.” They trust outputs they don’t fully understand. They give tools broad access and hope nothing breaks. When problems show up, such as security issues, inconsistent behavior, or unexpected logic, they fix them manually and move on.
That approach doesn’t scale.
That’s why we built Modern AI Development Techniques, a pathway launching January 7th.
The Problem Isn’t AI — It’s How We Use It
AI tools are powerful, but they are not developers.
They don’t understand your system goals, your threat model, or your organizational standards unless you make those things explicit. When instructions are vague, results are unpredictable. When constraints are missing, security becomes an afterthought.
We’ve seen this pattern repeatedly:
- AI-generated code that introduces vulnerabilities
- Prompts that can’t be reproduced or reused
- Agents that act quickly but without sufficient guardrails
- Confusion between models, tools, and frameworks
The issue isn’t model capability. It’s a lack of intentional development practices around AI.
What Modern AI Development Actually Looks Like
Modern AI Development Techniques was created to teach developers how to use AI deliberately, not experimentally.
This learning path focuses on practical techniques that bring structure, repeatability, and security to AI-augmented development. It introduces concepts such as vibe coding, AI agents, and the Model Context Protocol (MCP) through hands-on lessons grounded in real workflows, not theory.
The emphasis is clear:
- AI needs context
- AI needs constraints
- AI needs oversight
And to make those ideas actionable, the path is organized as a progression. Starting with how developers work with AI day-to-day, then moving into guardrails, automation, agents, and finally the protocols that make these workflows scalable.
Here’s what’s included in the learning path:
- AI/LLM | Introduction to Vibe Coding
- AI/LLM | Prompting Best Practices
- AI/LLM | Vibe Coding Rule Files
- AI/LLM | Vibe Coding Guardian
- AI/LLM | Intro to AI Agents
- AI/LLM | AI Agent Implementation
- AI/LLM | Introduction to MCP
- AI/LLM | MCP Architecture
From Prompts to Systems
The path begins with vibe coding, reframing how developers collaborate with AI. Instead of one-off prompts, learners focus on defining roles, intent, and boundaries so AI output becomes predictable and useful.
From there, prompting best practices show why clarity matters more than cleverness. Specific, structured instructions consistently outperform vague requests—especially when you’re building real systems instead of demos.
That structure extends into rule files, which encode security requirements, coding standards, and organizational expectations directly into AI workflows. Instead of correcting problems after code is generated, developers learn how to prevent them up front.
With Guardian, those rules evolve automatically, helping teams keep pace with a changing threat landscape without manual maintenance.
When AI Takes Action
As AI becomes more autonomous, the risks increase.
This path explores AI agents: systems that can plan, interact with their environment, execute tasks, and learn from prior actions. These capabilities enable powerful workflows, but only when access and autonomy are carefully controlled.
Learners see how agents are implemented in real codebases, how frameworks support them, and how to decide what an agent should—and should not—be allowed to do.
This leads to the Model Context Protocol (MCP), which standardizes how AI systems receive context and interact with tools. MCP provides a structured foundation for scaling AI workflows safely and predictably.
Build with AI On Purpose
AI will continue to evolve. Models will improve. Agents will gain more autonomy.
What matters is not how fast AI moves, but how deliberately developers use it. Modern AI Development Techniques was built to help teams do exactly that.
If AI is part of your development workflow in 2026, this path is designed to help you use it with confidence, control, and intent. Schedule a demo to learn more!
