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