enablement programYour path from zero to agentic AI
A guided, step-by-step roadmap for engineers new to generative AI. This portal does not teach the material itself — each step points you to the best videos, blogs, and official docs, so you always know what to learn next.
The learning path
Getting Started: GenAI & LLMs
Orient yourself before the deep dives. Build an accurate mental model of what generative AI and large language models actually are, how they are trained, how they differ from each other, and how to run one yourself.
5 topics
- 1
LLMs Deep Dive
Go under the hood. Understand how text becomes tokens, how a language model is actually built, and the transformer architecture and attention mechanism that make modern LLMs work.
3 topics
- 2
Retrieval-Augmented Generation (RAG)
Give models access to knowledge they were not trained on. Learn the full RAG pipeline end to end: embeddings, indexing, retrieval, and the advanced techniques that separate a demo from a production system.
8 topics
- 3
Prompt Engineering
Get reliable, high-quality output from models. Learn proven prompting patterns, how to manage the context window as it fills up, and how prompt caching cuts latency and cost.
3 topics
- 4
AI Agents
Move from single calls to systems that decide and act. Learn what makes something an agent, how tools and memory work, the difference between autonomous and workflow agents, human oversight, and the Model Context Protocol.
7 topics
- 5
Governance, Security & Deployment
Take systems to production responsibly. Learn to observe and trace agents, defend against the security risks unique to LLMs, add guardrails, evaluate quality rigorously, and deploy on managed agent platforms.
8 topics
- 6
Agentic Coding
Use AI agents to build software faster. Learn to drive coding agents effectively, master Claude Code, keep token usage and context lean, and adopt advanced patterns. Resources for this stage are being curated.
4 topics