The AI Engineering Masterclass
From LLMs to Production Systems
Everything you need to understand the AI engineering landscape. What to learn, how to think about it, and where to start building.
Includes Colab notebooks + AI engineering reference guide
The Agenda
LLMs and Prompt Engineering
Understand how LLMs are trained, why they hallucinate, and how to prompt them effectively. Structured outputs, prompt chaining, and knowing exactly when prompting is enough and when you need more.
RAG
Build a retrieval system that connects any data source to an LLM. Understand embeddings, vector search, chunking strategies, and every decision point in the pipeline. See it running live.
Tool Use and MCP
Make models take actions, not just generate text. Understand function calling, the tool execution loop, and MCP, the protocol that's standardizing how AI systems connect to tools and data.
Agents
The most overhyped and most misunderstood part of AI engineering. Understand the intelligence spectrum from fixed pipelines to autonomous agents. See what an agent actually looks like. Know when to use one and when not to.
Evaluation
AI systems are non-deterministic. You can't test them like traditional software. Understand how production teams measure AI quality, why most evaluation silently fails, and the skill 70% of AI interviews test for.
Context Engineering
Understand why every level in this session was about one thing: engineering what the model sees. Connect retrieval, tool use, agents, and evaluation into one framework you can apply to any AI system you build.
Built for engineers ready to build with AI
Engineers, architects, and technical leads who want to understand the full AI engineering landscape, not just fragments from blog posts
Anyone stuck between "I can call an API" and "I can build a production system" who needs the mental model to bridge the gap
Data engineers, data scientists, and backend engineers looking to add AI engineering to their skill stack
Anyone preparing for AI engineering roles who wants to know what companies actually hire for and what interviews actually test
Shivani Virdi
Former Software Engineer at Microsoft. Built and delivered the Engineer's RAG Accelerator to 50+ engineers from Microsoft, Adobe, Amazon, Shopify, Visa, and Autodesk. One engineer presented his production system to three VPs. Another was appointed to lead AI at his company.
83,000+ engineers follow her AI engineering content on LinkedIn. She quit Microsoft to teach AI engineering the way she wished she had learned it.
2.5 hours. 6 levels. The complete AI engineering landscape.
Thursday, April 16, 2026 | 11:00 AM ET. Online.
Save Your Spot | $29Includes Colab notebooks + AI engineering reference guide