The AI Engineering
Roadmap
11 categories · 26 vetted resources · 100% free.
👋 I'm Lakshman — AI Solution Owner designing and shipping enterprise AI systems for one of the largest healthcare companies in the world.
Most "learn AI" guides online are random YouTube playlists. This one isn't. It's the same path I'd take if I were starting over today — vetted, sequenced, and entirely free.
The order matters. Don't skip steps. Each category builds on the previous one.
GenAI Fundamentals
How AI actually works under the hood — the building blocks behind every model.
Prompt Engineering
The art of talking to LLMs to get reliable, high-quality output every single time.
Context Engineering
Designing the
AI Coding & Vibe Code
Orchestrating AI to turn natural-language specs into production code in hours, not weeks.
AI Agents
AI that plans tasks, calls tools, recovers from errors, and finishes multi-step work autonomously.
RAG Basics
Connecting LLMs to your own data — docs, tickets, codebase — so they answer with facts, not hallucinations.
Multimodal AI Systems
AI that reads images, watches video, hears audio — not just text in/text out.
AI Safety & Security
Protecting AI systems from prompt injection, jailbreaks, data leaks, and tool abuse.
AI Infra & LLMOps
Running AI in production — managing cost, latency, monitoring, deployment pipelines.
Fine-Tuning & Post-Training
Customizing models for narrow domains, specific tones, or latency-sensitive small-model use cases.
AI Evaluations
Measuring how well your AI actually works on hundreds of real cases — not just demo vibes.
Start building. Then come back.
I'll keep this roadmap updated as the landscape moves. New drops on agents, prompts, tools, and the war stories from shipping AI in regulated production.