Opleiding: AI+ Context Engineering™

Master AI+ Context Engineering for Production-Grade AI Systems

• Context Strategy & Architecture: Learn how to design robust context architectures that go beyond prompts—managing instructions, memory, tools, and knowledge for reliable AI behavior across sessions and workflows.
• Building Context-Aware AI Systems: Gain hands-on skills in implementing context pipelines, RAG architecture, and memory systems that ensure grounded, accurate, and cost-efficient AI outputs.
• Context Management & Optimization: Master the Write-Select-Compress-Isolate (W-S-C-I) framework to control relevance, reduce hallucinations, optimize token usage, and scale AI systems effectively.
• Enterprise-Grade Context Integration: Learn how to integrate AI safely into enterprise environments with role-based access, compliance guardrails, secure memory, and conflict-free context orchestration.
• Future-Ready Agent & Workflow Design: Prepare for the next wave of AI by designing multi-agent systems, automated workflows, and context-driven architectures that remain reliable as models, tools, and scale evolve.

• 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

• 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

• 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

• 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

• 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

• 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)

• 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

• 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

• 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.