Basic RAG is easy. High-fidelity RAG that retrieves accurately, cites sources, prevents hallucination, and works at scale is hard. This live 6-hour workshop teaches you to build production-grade RAG pipelines as part of a complete multi-agent context engineering system.
By Packt Publishing · Refunds up to 10 days before
A basic RAG pipeline retrieves chunks and passes them to an LLM. A high-fidelity RAG pipeline manages context windows carefully, cites sources, validates retrieval quality, handles memory across turns, and prevents hallucination — the difference between a prototype and a production system.
Context engineering is the discipline of designing AI systems that provide the right information, tools, and context to LLMs at the right time — replacing brittle prompts with reliable, scalable production AI architectures.
Multi-agent systems are AI architectures where specialised agents collaborate to accomplish complex tasks. This workshop shows you how to orchestrate them reliably using the Model Context Protocol and semantic blueprints.
MCP is Anthropic's open standard for connecting AI models to tools, data sources, and other agents. This workshop teaches you to use MCP for building orchestrated multi-agent workflows that are transparent and controllable.
Context engineering and multi-agent systems have almost no quality hands-on resources. This 6-hour live workshop gives you a complete guided build with a bestselling AI author answering your questions throughout.
Six modules. Six hours. A production-ready context engine by the time you finish.
Design structured context that gives AI agents precise, goal-driven contextual awareness beyond simple prompting.
Orchestrate specialised agents using the Model Context Protocol for adaptable, context-rich reasoning workflows.
Engineer retrieval-augmented generation pipelines with citations, memory, and safeguards against hallucination.
Design AI memory systems that maintain context across long conversations and complex multi-step workflows.
Implement moderation, data poisoning protection, prompt injection prevention, and trust mechanisms for production AI.
Build a transparent, traceable Context Engine that gives you complete visibility and control over your AI system.
A working production system — not just architectural knowledge.
A fully working multi-agent system with context engineering
MCP-orchestrated agent workflows you can use in production
High-fidelity RAG pipeline with citations and memory
Semantic blueprints and agent architecture patterns
Production-ready safeguards against hallucination and injection
Certificate of completion from Packt Publishing
Denis Rothman has built and written about production RAG systems — the instructor you want for serious RAG architecture.
Denis Rothman is a bestselling AI author with over 30 years of experience in artificial intelligence, optimisation, and agent systems. He has written multiple cutting-edge AI books for Packt Publishing and is the author of the book “Context Engineering for Multi-Agent Systems.” In this workshop he guides you step by step through the practical architecture of production-ready multi-agent AI systems.
This is an intermediate to advanced workshop. You need the basics below.
Common questions about the workshop, what to expect, and how to prepare.
Basic RAG retrieves text chunks and passes them to an LLM hoping for a good answer. High-fidelity RAG manages context windows to avoid overflow, validates retrieval relevance, cites specific sources for every claim, handles multi-turn memory across conversations, and includes safeguards to detect and prevent hallucination. This workshop builds the high-fidelity version.
RAG pipelines hallucinate when retrieved context is irrelevant, when the LLM ignores retrieved context and relies on training data instead, or when context windows overflow causing the model to lose track of retrieved information. This workshop covers hallucination detection, relevance validation, context window management, and citation verification to prevent these failure modes.
The workshop covers RAG pipeline architecture that works with any knowledge source — documents, databases, APIs, or structured data. The instructor focuses on the architectural patterns and context engineering principles that make RAG reliable, and demonstrates with concrete examples you can adapt to your own knowledge sources.
Memory engineering allows your RAG pipeline to maintain context across multiple conversation turns, building a richer picture of user intent and conversation history. This prevents the common failure mode of treating each query in isolation and enables more accurate, contextually appropriate retrieval.
Yes. The RAG pipeline architecture and context engineering patterns taught in this workshop are designed to be modular and reusable. The instructor covers how to adapt the pipeline for different use cases and integrate it with existing AI systems after the session.
Yes. The workshop covers the vector storage and retrieval components of RAG pipelines including embedding models, similarity search, and retrieval quality evaluation. The instructor covers both the technical implementation and the context engineering principles that determine what gets retrieved and how it is presented to the LLM.
6 hours. Live bestselling AI author. High-fidelity RAG pipeline working by the end. Seats are limited.
Register Now →Saturday April 25 · 9am to 3pm EDT · Online · Packt Publishing