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Welcome to the GenAI Learning Path.

If you are opening this site with a vague goal like "we need to understand AI better" or a very specific one like "we need to ship a grounded internal copilot without creating a compliance headache," this page is the right place to begin.

This learning path is designed to move from shared fundamentals into real implementation patterns: models, prompting, retrieval, architecture, governance, and enterprise rollout.

Who This Is For​

  • Engineers building GenAI features, copilots, RAG systems, or LLM-backed applications
  • Architects designing reliable, governable, production-ready AI platforms
  • Technical leads and product owners deciding where GenAI fits, what it should do, and how to scale it responsibly

How To Use This Learning Path​

  1. Start with Foundations to build shared vocabulary and the right mental model.
  2. Move into LLM Core and Prompt Engineering to understand model behavior and improve output reliability.
  3. Continue into MCP, RAG, model selection, and fine-tuning as your systems become more connected and more specialized.
  4. Use the Enterprise sections when you need architecture, governance, operational readiness, and rollout guidance.

If you are unsure where to start, use this order:

  1. Generative AI Ecosystem
  2. How LLMs Work
  3. Prompt Engineering Core

That sequence gives you the map, the model mechanics, and the day-to-day control surface.

Choose Your Route​

Route 1: Build a Strong Foundation​

Best if you are new to GenAI or onboarding a team.

  1. Generative AI Ecosystem
  2. Unveiling GenAI
  3. What Is Generative AI?
  4. How Generative AI Works

Route 2: Build Better LLM Systems​

Best if you already know the basics and want to improve quality, reliability, and integration.

  1. How LLMs Work
  2. Function Calling and Structured Output
  3. Context Management for LLM Systems
  4. LLM Evaluation and Observability

Route 3: Build Enterprise-Ready AI​

Best if your questions are about trust, scale, governance, and platform design.

  1. RAG Fundamentals
  2. Intro to Enterprise Architecture
  3. Intro to Governance
  4. Intro to Enterprise Strategy

Example Learning Journey​

An engineering team building an internal policy assistant could use the path like this:

  1. Start with Foundations to understand the ecosystem, grounding, and model limitations.
  2. Use LLM Core to understand context windows, tool use, evaluation, and failure modes.
  3. Use Prompt Engineering and RAG to make the assistant reliable on company knowledge.
  4. Use Governance and Enterprise Readiness guidance before broader rollout.

What Success Looks Like​

By the time you move through the early modules, you should be able to:

  • explain GenAI and LLM concepts clearly to both technical and non-technical stakeholders
  • choose between prompting, retrieval, tool use, fine-tuning, and model routing based on real constraints
  • recognize the difference between a strong demo and a system that can survive production traffic
  • design with cost, latency, trust, and compliance in mind from the start

Next Step​

Begin with Generative AI Ecosystem.