Context Engineering Masterclass: LLMs, RAG & Agents

Breaks down token costs and economics to help you budget context usage efficiently.

Explores the "lost in the middle" problem, where LLMs overlook information buried in long contexts.

Shows how overloading context dilutes the model's attention and hurts response quality.

Compares quality vs. quantity of context, showing why more information isn't always better.

Introduces metrics and techniques for measuring the performance impact of your context strategy.

Hands-on project auditing an existing context setup to identify waste and improvement opportunities.

Introduces the six distinct types of context every well-engineered prompt should consider.

Covers the optimal order for assembling context pieces so the model reads the most relevant info first.

Teaches how to deliver the right information at the right moment in a conversation or workflow.

Provides reusable context templates to speed up building consistent, high-quality prompts.

A hands-on demo project assembling a complete context pipeline, wrapping up the section.

Reveals the five silent killers that make most RAG implementations underperform.

Deep dive into chunking strategies with a hands-on demo.

How chunk size, overlap, and metadata choices affect retrieval quality.

Comparing semantic, keyword, and hybrid search for better retrieval.

A deep dive into reranking and how it boosted this RAG system's performance.

Techniques for compressing context to cut costs while preserving relevance.

Hands-on project assembling a complete engineered RAG system.

Why memory matters for building context-aware AI agents.

How to manage conversation history as a memory type for agents.

Implementing short-term memory to keep track of recent context.

Building long-term memory so agents retain knowledge across sessions.

Strategies for retrieving the right memories at the right time.

Patterns for injecting retrieved memories back into the context window.

Hands-on project building a chatbot with a complete memory system.

Key differences between context for autonomous agents and traditional context.

Managing tool context to keep agent tool calls efficient and accurate.

Chaining tool results together to solve multi-step tasks.

Maintaining memory across multiple turns of an agent conversation.

Sharing context effectively across multi-agent systems.

How to isolate context between agents to avoid interference.

Hands-on project building a complete agent context management system.

Learn strategies to gracefully handle errors and failures in LLM applications.

Implement rate limiting techniques to manage API usage and prevent throttling.

Explore techniques to monitor and optimize LLM API costs at scale.

Design systems that degrade gracefully under failure to maintain reliability.

Master retry patterns with backoff strategies to build resilient LLM systems.

Build a complete production-grade system applying all production patterns learned.

Establish organization-wide standards for context engineering, including naming conventions, documentation practices, and governance frameworks that keep AI systems consistent and maintainable at scale.

Design context systems that safely serve multiple customers or business units from shared infrastructure. Covers data isolation, tenant-aware retrieval, and access control patterns for multi-tenant AI applications.

Understand compliance and security requirements for enterprise AI, including data privacy, prompt injection defense, PII redaction, and audit logging needed to meet regulatory standards.

Learn how to version context templates, prompts, and retrieval configurations safely in production. Covers change tracking, rollback strategies, and backward compatibility for evolving AI systems.

Build reusable, standardized context modules and prompt libraries that scale across teams and projects. Learn packaging, versioning, and distribution patterns for shared enterprise context assets.

Explore testing strategies for context-driven AI systems, including regression tests, retrieval quality checks, and validation pipelines that catch context failures before they reach production.

Capstone project: design and build a complete enterprise context engineering platform. Apply standards, multi-tenancy, security, versioning, and testing concepts from this section into a working end-to-end system.