AI+ Context Engineering™
Master AI+ Context Engineering for Production-Grade AI Systems
Highlights
About this course
- 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.
This course includes
Course curriculum
8 chapters · 55 lessons
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
AI Tools Used
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
Prerequisites
Exam Details
Mode of Learning
Delivery: SelfPaced
- ✓ Instructor-led OR Self-paced course
- ✓ Official exam
- ✓ Digital badge