An AI roadmap for engineers. Go from foundational concepts to advanced topics like LLM Ops, RAG, and AI security. Your guide to building AI.
AI content is everywhere. Most of it is noise — surface takes, marketing hype, or dense academic papers. If you’re a developer, architect, or engineer who wants depth, the signal is hard to find.
This series is the signal.
More importantly, it’s a structured 20-part roadmap built for technical professionals. Instead of fluff, we’ll focus on the knowledge you need to build, deploy, and manage AI systems. We’ll start with the fundamentals, and then move to the messy reality of production.
This first post serves as the roadmap. It’s a living hub that will link to every article as it goes live. As a result, you’ll always have one place to track progress. Bookmark it.
The Roadmap
The series has two tiers. First, the foundations. Then, the advanced applied work you’ll face in production.
Tier 1: Core Foundations (Posts 1–17)
This is the base layer. You can’t build a solid system without it.
- A Brief History of AI: Winters, Breakthroughs, and the Agentic Future — Read it here
- AI vs ML vs Deep Learning: What’s the Difference? — Read it here
- Machine Learning Paradigms: Supervised, Unsupervised, Reinforcement — [Coming soon]
- Neural Networks Explained, Simply — [Coming soon]
- The Deep Learning Revolution: From AlexNet to Transformers — [Coming soon]
- The Generative AI Era: Text, Image, and Code Creation — [Coming soon]
- How Do Large Language Models (LLMs) Actually Work? — [Coming soon]
- The AI Tech Stack: An Engineer’s Guide — [Coming soon]
- Key Concepts: Tokens, Embeddings, and Vector Databases — [Coming soon]
- Different Types of AI Models (And When to Use Them) — [Coming soon]
- The Role of Data in AI: Garbage In, Garbage Out — [Coming soon]
- AI in Action: NLP, Computer Vision, and Robotics — [Coming soon]
- AI Ethics for Engineers: Bias, Fairness, and Transparency — [Coming soon]
- Ethics in AI: Bias, Privacy, and Trustworthy Systems — [Coming soon]
- The Future of AI: Multimodal Models and AI Agents — [Coming soon]
- How to Start Learning AI: A Practical Path — [Coming soon]
- Your AI Learning Path: Math, Code, and Projects — [Coming soon]
Tier 2: Advanced & Applied Topics (Posts 18–28)
With the foundations in place, we shift to tools, patterns, and production challenges. For example, monitoring large models in the wild or defending against prompt injection.
- AI Tooling in Practice: Jupyter, Hugging Face, and LangChain Explained — [Coming soon]
- Data Engineering for AI: Feature Stores, Pipelines, and Vector Databases — [Coming soon]
- Prompt Engineering: Techniques That Actually Work — [Coming soon]
- LLM Ops: Monitoring, Fine-Tuning, and Hallucination Defense — [Coming soon]
- AI for Developers: Copilot, Debugging, and Workflow Automation — [Coming soon]
- Enterprise AI Patterns: RAG, Agents, and System Integration — [Coming soon]
- AI Security: Adversarial Attacks and Prompt Injection Explained — [Coming soon]
- AI Failures: What Tay, Biased Hiring, and Chatbots Teach Us — [Coming soon]
- AI in Production: Drift, Retraining, and Scaling to Millions of Users — [Coming soon]
- AI and Your Career: Skills to Bet On in the Next Decade — [Coming soon]
- Adversarial AI: How Hackers Break Models and How to Defend Them — [Coming soon]
Tier 3: Closing Synthesis (Post 29)
Lessons Learned Across 29 Posts: An AI Engineer’s Toolkit — [Coming soon]
This Is a Living Document
This post is the central hub for the series. As each article is published, links will be added here. In addition, updates will refine the roadmap as the field evolves.
This isn’t about hype. It’s a practical playbook for building with AI.
Let’s get started.
