Learn AI, at your level
From fundamentals to frontier architecture — a curated curriculum to understand, build, and deploy AI. No fluff, just signal.
Learning paths
Choose your journey based on where you are today
What is an LLM and why should you care?
The foundational technology behind every AI product you interact with.
RAG explained: Making AI useful for your business
How to make AI answer questions about your specific company data.
AI agents vs chatbots: What's the real difference?
Understanding autonomous AI agents and why they're the next frontier.
How to evaluate AI tools without getting burned
A framework for choosing tools that actually deliver ROI.
Understanding tokens, context windows, and costs
The economics of AI — know what you're paying for and why.
AI safety and alignment: A practical primer
Why safety research matters and how it affects the products you use.
Building your first RAG pipeline
Step-by-step guide to connecting your knowledge base to an LLM.
AI automation playbook for operations
Identify and implement your highest-ROI automation opportunities.
Prompt engineering that actually works
Systematic techniques for reliable, high-quality AI outputs.
Measuring AI ROI: Practical frameworks
Track and justify your AI investments with data, not hype.
Building an AI-first support stack
Deploy AI support that actually resolves tickets — architecture to metrics.
Vector databases demystified
The storage layer powering semantic search and RAG systems.
Transformer architecture deep dive
Understand the engine behind modern AI models.
Fine-tuning vs RAG vs prompting
Choose the right approach for your data and constraints.
Building agentic systems: Architecture patterns
Design patterns for tool-use, planning, and memory in AI agents.
Scaling inference: Prototype to production
Latency optimization, caching, batching, and cost management.
Evaluation frameworks for LLM applications
How to measure whether your AI system is actually working.
Multi-agent orchestration patterns
Coordinate multiple AI agents for complex, multi-step workflows.
Key concepts
Essential vocabulary for navigating the AI landscape
Transformer
The neural network architecture behind GPT, Claude, Gemini, and virtually all modern LLMs.
RAG
Retrieval-Augmented Generation — grounding LLM responses in your own data for accurate, specific answers.
Fine-tuning
Training a pre-existing model on your specific data to specialize its behavior and knowledge.
Context window
The amount of text a model can process at once — from 4K tokens (early GPT) to 1M+ (Claude 4.6).
Agentic AI
AI systems that autonomously plan, use tools, and take actions to accomplish complex goals.
RLHF
Reinforcement Learning from Human Feedback — the technique that makes AI helpful, harmless, and honest.
Recommended reading order
A structured path from zero to deep understanding
Foundation
Beginner- What is an LLM
- Understanding tokens & costs
- RAG explained
Application
Operator- Prompt engineering
- Building your first RAG pipeline
- Measuring AI ROI
Architecture
Technical- Transformer deep dive
- Fine-tuning vs RAG vs prompting
- Agentic systems patterns
Ready to go deeper?
Explore the economics dashboard or check today's AI developments.