AI+ Context Engineering™

Target group

AI Engineers & LLM Developers: Built for practitioners who want to move beyond basic prompt engineering and design production-grade, context-aware AI systems using RAG, memory, tools, and orchestration patterns

Product Managers & AI Architects: Ideal for professionals responsible for shipping reliable AI features who need to understand context pipelines, grounding, cost control, and system-level design tradeoffs rather than toy demos

Data & Platform Engineers: For engineers working with vector databases, embeddings, retrieval systems, and AI infrastructure who want to architect scalable, efficient, and trustworthy context flows

Enterprise & Solution Architects: Designed for architects building AI systems in regulated or large-scale environments who must manage security, compliance, cost optimization, and multi-agent orchestration

AI Consultants & Technical Leaders: For professionals advising organizations on AI adoption who need a deep, practical understanding of why context—not just models—is the real differentiator in modern AI systems

Advanced No-Code / Automation Builders: A strong fit for builders using tools like n8n, Make, or Zapier who want to design reliable AI workflows and agentic systems without writing heavy infrastructure code

Course structure

Module 1: Foundations of Context Engineering – Introduction

1. 1.1 What is Context Engineering (Beyond Prompt Engineering)
2. 1.2 From Prompting to Context Pipelines: The 2025 Paradigm Shift
3. 1.3 The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
4. 1.4 Short-Term vs Long-Term Memory in LLM Systems
5. 1.5 Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
6. 1.6 Use Case: Context-Aware AI Travel Assistant
7. 1.7 Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent

Module 2: Context Management Patterns & Techniques

1. 2.1 The W-S-C-I Framework: Write, Select, Compress, Isolate
2. 2.2 WRITE Strategy: Agent Identity, Persona, Guardrails, and State
3. 2.3 SELECT Strategy: Precision Retrieval & Metadata Filtering
4. 2.4 COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
5. 2.5 ISOLATE Strategy: Context Boundaries, Safety, and Focus
6. 2.6 Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
7. 2.7 Case Study: ChatGPT & Claude Memory Systems
8. 2.8 Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex

Module 3: Context Pipelines, RAG & Grounding Architecture

1. 3.1 The End-to-End Context Pipeline (Input → Retrieval → Compression → Assembly → Response → Update)
2. 3.2 Retrieval-Augmented Generation (RAG) Architecture Deep Dive
3. 3.3 Vector Databases: Pinecone, Chroma & Embedding Models
4. 3.4 Grounding Failures: Hallucinations, Context Poisoning, Distraction
5. 3.5 Mitigation Techniques: Rerankers, Provenance, Context Forensics
6. 3.6 Case Study: Anthropic’s Multi-Agent Researcher (MAR)
7. 3.7 Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses

Module 4: Optimization, Scaling & Enterprise Readiness

1. 4.1 Token Economy & Cost Optimization in Context Pipelines
2. 4.2 Context Scaling & the Model Context Protocol (MCP)
3. 4.3 Security & Compliance: PII Filtering, Redaction, Role-Based Access
4. 4.4 Conflict Resolution & Context Consistency
5. 4.5 Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
6. 4.6 Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
7. 4.7 Hands-on: Implement Role-Based Context Filtering and Secure Retrieval

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

1. 5.1 Translating Business Processes into AI-Ready Context Flows
2. 5.2 Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
3. 5.3 Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
4. 5.4 Context Templates for Consistency & Structured Outputs
5. 5.5 Use Case: Dynamic Customer Onboarding Assistant
6. 5.6 Case Studies: Airbnb Support Automation & HSBC SME Lending
7. 5.7 Hands-on: Build a Context Flow Using No-Code Orchestration

Module 6: Real-World Industry Context Applications

1. 6.1 Context Engineering in Regulated Domains
2. 6.2 Healthcare: Clinical Decision Support & PHI Isolation
3. 6.3 Finance: Market Analysis, Compliance Summarization & Tool-Based Context
4. 6.4 Legal & Education: Precision Retrieval & Personalized Learning Context
5. 6.5 Risk Mitigation: Context Poisoning & Context Clash
6. 6.6 Advanced Agent Memory for Long-Horizon Tasks
7. 6.7 Case Studies: Activeloop (Legal/IP) & Five Sigma (Insurance)

Module 7: Multi-Agent Orchestration & the Future

1. 7.1 Why Monolithic Agents Fail: Context Explosion
2. 7.2 Multi-Agent Systems (MAS) & Context Isolation
3. 7.3 Agent Roles: Router, Planner, Executor
4. 7.4 Agent-to-Agent Context Compression
5. 7.5 Guardrails, Governance & Inter-Agent Safety
6. 7.6 Ethics, Bias Mitigation & Source Traceability
7. 7.7 Case Studies: IBM Watson Orchestrate & Enterprise Context Orchestrators
8. 7.8 Career Pathways: Context Architect & AI Governance Roles

Module 8: Capstone Project & Certification

1. 8.1 Capstone Overview: Multi-Agent Context-Aware System
2. 8.2 Build: Query Router with Financial Calculations & Policy RAG (n8n)
3. 8.3 Presentation, Review & Feedback
4. 8.4 Final Evaluation & AI+ Context Engineering Certification

Certification

50 questions, 70% passing, 90 minutes, online proctored exam