AI+ Context Engineering™

Master AI+ Context Engineering for Production-Grade AI Systems

Learn to design strong context architectures that go beyond prompts by structuring instructions, memory, and knowledge for consistent AI behavior. Build practical skills in context pipelines, RAG, and memory systems to deliver accurate and efficient outputs. Master the Write-Select-Compress-Isolate framework to control relevance and reduce hallucinations. Integrate AI safely into enterprise environments with role-based access and compliance guardrails. Finally, prepare for the future by creating multi-agent systems and automated workflows that scale as models and tools evolve.

Module 1: Foundations of Context Engineering – Introduction

• 1.1 What is Context Engineering (Beyond Prompt Engineering)
• 1.2 From Prompting to Context Pipelines: The 2025 Paradigm Shift
• 1.3 The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
• 1.4 Short-Term vs Long-Term Memory in LLM Systems
• 1.5 Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
• 1.6 Use Case: Context-Aware AI Travel Assistant
• 1.7 Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent

Module 2: Context Management Patterns & Techniques

• 2.1 The W-S-C-I Framework: Write, Select, Compress, Isolate
• 2.2 WRITE Strategy: Agent Identity, Persona, Guardrails, and State
• 2.3 SELECT Strategy: Precision Retrieval & Metadata Filtering
• 2.4 COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
• 2.5 ISOLATE Strategy: Context Boundaries, Safety, and Focus
• 2.6 Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
• 2.7 Case Study: ChatGPT & Claude Memory Systems
• 2.8 Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex

Module 3: Context Pipelines, RAG & Grounding Architecture

• 3.1 The End-to-End Context Pipeline (Input → Retrieval → Compression → Assembly → Response → Update)
• 3.2 Retrieval-Augmented Generation (RAG) Architecture Deep Dive
• 3.3 Vector Databases: Pinecone, Chroma & Embedding Models
• 3.4 Grounding Failures: Hallucinations, Context Poisoning, Distraction
• 3.5 Mitigation Techniques: Rerankers, Provenance, Context Forensics
• 3.6 Case Study: Anthropic’s Multi-Agent Researcher (MAR)
• 3.7 Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses

Module 4: Optimization, Scaling & Enterprise Readiness

• 4.1 Token Economy & Cost Optimization in Context Pipelines
• 4.2 Context Scaling & the Model Context Protocol (MCP)
• 4.3 Security & Compliance: PII Filtering, Redaction, Role-Based Access
• 4.4 Conflict Resolution & Context Consistency
• 4.5 Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
• 4.6 Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
• 4.7 Hands-on: Implement Role-Based Context Filtering and Secure Retrieval

Module 5: Context Flow Design for Business Users (No-Code AI)

• 5.1 Translating Business Processes into AI-Ready Context Flows
• 5.2 Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
• 5.3 Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
• 5.4 Context Templates for Consistency & Structured Outputs
• 5.5 Use Case: Dynamic Customer Onboarding Assistant
• 5.6 Case Studies: Airbnb Support Automation & HSBC SME Lending
• 5.7 Hands-on: Build a Context Flow Using No-Code Orchestration

Module 6: Real-World Industry Context Applications

• 6.1 Context Engineering in Regulated Domains
• 6.2 Healthcare: Clinical Decision Support & PHI Isolation
• 6.3 Finance: Market Analysis, Compliance Summarization & Tool-Based Context
• 6.4 Legal & Education: Precision Retrieval & Personalized Learning Context
• 6.5 Risk Mitigation: Context Poisoning & Context Clash
• 6.6 Advanced Agent Memory for Long-Horizon Tasks
• 6.7 Case Studies: Activeloop (Legal/IP) & Five Sigma (Insurance)

Module 7: Multi-Agent Orchestration & the Future

• 7.1 Why Monolithic Agents Fail: Context Explosion
• 7.2 Multi-Agent Systems (MAS) & Context Isolation
• 7.3 Agent Roles: Router, Planner, Executor
• 7.4 Agent-to-Agent Context Compression
• 7.5 Guardrails, Governance & Inter-Agent Safety
• 7.6 Ethics, Bias Mitigation & Source Traceability
• 7.7 Case Studies: IBM Watson Orchestrate & Enterprise Context Orchestrators
• 7.8 Career Pathways: Context Architect & AI Governance Roles

Module 8: Capstone Project & Certification

• 8.1 Capstone Overview: Multi-Agent Context-Aware System
• 8.2 Build: Query Router with Financial Calculations & Policy RAG (n8n)
• 8.3 Presentation, Review & Feedback
• 8.4 Final Evaluation & AI+ Context Engineering Certification

Tools you will explore

• LangChain and LangGraph
• LlamaIndex
• Vector Databases (Pinecone, Chroma)
• n8n, Zapier, Make.com
• Embedding Models and RAG Pipelines
• No-Code Automation Platforms
• Enterprise Data and API Integrations

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Instructor-led OR Self-paced course + Official exam + Digital badge