The AI Playbook: A Roadmap from Foundations to Production
This series presents a structured AI roadmap designed for engineers and technical professionals, emphasizing essential concepts and advanced applications like LLM Ops and AI security. The content focu
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 —
- Neural Networks Explained, Simply —
- The Deep Learning Revolution: From AlexNet to Transformers —
- The Generative AI Era: Text, Image, and Code Creation —
- How Do Large Language Models (LLMs) Actually Work? —
- The AI Tech Stack: An Engineer’s Guide —
- Key Concepts: Tokens, Embeddings, and Vector Databases —
- Different Types of AI Models (And When to Use Them) —
- The Role of Data in AI: Garbage In, Garbage Out —
- AI in Action: NLP, Computer Vision, and Robotics —
- AI Ethics for Engineers: Bias, Fairness, and Transparency —
- Ethics in AI: Bias, Privacy, and Trustworthy Systems —
- The Future of AI: Multimodal Models and AI Agents —
- How to Start Learning AI: A Practical Path —
- Your AI Learning Path: Math, Code, and Projects —
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 —
- Data Engineering for AI: Feature Stores, Pipelines, and Vector Databases —
- Prompt Engineering: Techniques That Actually Work —
- LLM Ops: Monitoring, Fine-Tuning, and Hallucination Defense —
- AI for Developers: Copilot, Debugging, and Workflow Automation —
- Enterprise AI Patterns: RAG, Agents, and System Integration —
- AI Security: Adversarial Attacks and Prompt Injection Explained —
- AI Failures: What Tay, Biased Hiring, and Chatbots Teach Us —
- AI in Production: Drift, Retraining, and Scaling to Millions of Users —
- AI and Your Career: Skills to Bet On in the Next Decade —
- Adversarial AI: How Hackers Break Models and How to Defend Them —
Tier 3: Closing Synthesis (Post 29)
Lessons Learned Across 29 Posts: An AI Engineer’s Toolkit —
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.