Dr. Greg and The Wiz Maven – The AI Engineering Bootcamp (2025)

– Understand course structure – How to succeed as a certified AI Engineer on Demo Day!

– Meet your cohort, peer supporters, and journey group!

– LLM prototyping best practices, how to spin up quick end-to-end prototypes and vibe check them!

– Prompt Engineering best practices and the LLM Application Stack

– Understand embedding models and similarity search

– Understand Retrieval Augmented Generation = Dense Vector Retrieval + In-Context Learning

– Build a Python RAG app from scratch

– Understand the state of production LLM application use cases in industry
– Ideate with peers & peer supporters

– Build an end-to-end RAG application using everything we’ve learned so far

– Why LangChain, OpenAI, QDrant, LangSmith

– Understand LangChain & LangGraph core constructs

– Introduce primary cohort use case

– Build a RAG system with LangChain and Qdrant

– Intro to LangSmith for evaluation and monitoring

– Answer the question: “What is an agent?”

– Understand how to build production-grade agent applications using LangGraph

– Understand Context Engineering from first principles

– How to use LangSmith to evaluate agentic RAG applications

– Understand what multi-agent systems are and how they operate.

– Extend the primary cohort use case to a multi-agent solution

– Build a production-grade multi-agent applications using LangGraph

– An overview of Synthetic Data Generation (SDG) – How to use SDG for Evaluation

– Generating high-quality synthetic test data sets for RAG applications in general and for our primary cohort use case specifically

– How to use LangSmith to baseline performance, make improvements, and then compare

– Build RAG and Agent applications with LangGraph
– Evaluate RAG and Agent applications quantitatively with the RAG ASsessment (RAGAS) framework
– Use metrics-driven development to improve agentic applications, measurably, with RAGAS

– Understand how advanced retrieval and chunking techniques can enhance RAG

– Compare the performance of retrieval algorithms for RAG

– Understand the fine lines between chunking, retrieval, and ranking for our primary cohort use case

– Learn best practices for retrieval pipelines

– Discuss best-practice use of reasoning models
– Understand planning and reflection agents
– Build an Open-Source Deep Research agent application using LangGraph
– Investigate evaluating complex agent applications with the latest tools

– Introduce the Certification Challenge
– Pitch your problem, audience, and solution
– Network with people outside of your group

– Understand the suite of tools for building agents with OpenAI and the evolution of their tooling
– Core constructs of the Agents SDK and comparison to other agent frameworks
– How to use monitoring and observability tools on the OpenAI platform

– Understand Model Context Protocol (MCP) from client and server sides

– Learn MCP resource types and how to design useful MCP servers

– Build an MCP server that enhances allows us to enhance our search and retrieval toolbox for our primary cohort use case

– Learning how to monitor, visualize, debug, and interact with your LLM applications with LangSmith and LangGraph Studio

– Deploy your applications to APIs directly via LangGraph Platform

– Deploy an agent with MCP server for our primary cohort use case

– Defining LLM Operations (LLM Ops) & Agent Operations (Agent Ops)

– Understand Agent2Agent Protocol (A2A), including how remote agents and client agents interact

– How to enable agent-to-agent communication with Agent cards and MCP

– Deploy an agent with MCP server for our primary cohort use case and then build another agent to act as a user of our application

– Understand guardrails, including the key categories of guardrails

– Understand the importance of caching – How to use Prompt and Embedding caching

– Apply guardrails and caching directly to our primary cohort use case

– Understand how to deploy open LLMs and embeddings to scalable endpoints

– Discuss how to choose inference server

– Build an E2E enterprise agentic RAG application with LCEL

– Introduction to Building On-Prem

– Hardware & compute Considerations

– Local LLM & Embedding Model Hosting Comparison

– How to build and present an On-Prem Solution to stakeholders

– Code Freeze
– Full Dress Rehearsal

Live AI Engineering Demo Day with industry judges, the public, and a cohort of your peers!