AI+ Context Engineering - eLearning (exam included)

Course timeline

1. Foundations of Context Engineering

   Lesson 1

   • Introduction to Context Engineering beyond traditional prompt engineering
   • The shift from simple prompting to full context pipelines
   • Core elements of context: instructions, knowledge, tools, and system state
   • Short-term versus long-term memory in LLM-based systems
   • Key advantages: grounding, relevance, continuity, and cost efficiency
   • Use Case: Designing a context-aware AI travel assistant
   • Hands-on: Creating system instructions and memory states for a role-based AI agent

2. Context Management Frameworks & Methods

   Lesson 2

   • The W-S-C-I framework: Write, Select, Compress, Isolate
   • WRITE: Defining agent identity, persona, guardrails, and state control
   • SELECT: High-precision retrieval and metadata filtering
   • COMPRESS: Summarization, token optimization, and auto-compaction
   • ISOLATE: Setting boundaries for safety, focus, and context protection
   • Advanced retrieval strategies: hybrid search and semantic chunking
   • Case Study: Memory systems in ChatGPT and Claude
   • Hands-on: Applying context selection and compression with LangChain or LlamaIndex

3. Context Pipelines, RAG & Grounded AI Architecture

   Lesson 3

   • Designing the complete context pipeline (input → retrieval → compression → assembly → response → update)
   • Deep dive into Retrieval-Augmented Generation (RAG) systems
   • Working with vector databases such as Pinecone and Chroma, and embedding models
   • Identifying grounding failures: hallucinations, context poisoning, distraction
   • Mitigation techniques: reranking, provenance tracking, and context diagnostics
   • Case Study: Anthropic’s Multi-Agent Researcher (MAR)
   • Hands-on: Building a RAG pipeline with vector search and grounded outputs

4. Optimization, Scaling & Enterprise Deployment

   Lesson 4

   • Managing token usage and cost optimization strategies
   • Context scaling and the Model Context Protocol (MCP)
   • Security and compliance: PII filtering, redaction, and role-based access
   • Conflict resolution and maintaining context consistency
   • Handling multi-modal context (text, tables, PDFs, video transcripts)
   • Case Studies: Walmart “Ask Sam” and Morgan Stanley Knowledge Assistant
   • Hands-on: Implementing secure, role-based context filtering and retrieval

5. Context Flow Design for Business & No-Code Users

   Lesson 5

   • Converting business processes into AI-ready context workflows
   • Context Flow Diagrams (CFDs) and Automated Workflow Architecture (AWA)
   • Visual implementation of W-S-C-I using no-code tools (n8n, Make, Zapier)
   • Using context templates for structured and consistent outputs
   • Use Case: Building a dynamic customer onboarding assistant
   • Case Studies: Airbnb support automation and HSBC SME lending
   • Hands-on: Creating a context flow using no-code orchestration tools

6. Industry Applications of Context Engineering

   Lesson 6

   • Applying context engineering in regulated environments
   • Healthcare: clinical decision support and PHI isolation
   • Finance: compliance summarization, market analysis, and tool-based context
   • Legal & education: precision retrieval and personalized learning systems
   • Risk mitigation: handling context poisoning and context conflicts
   • Designing advanced agent memory for long-horizon tasks
   • Case Studies: Activeloop (Legal/IP) and Five Sigma (Insurance)

7. Multi-Agent Systems & Future Architectures

   Lesson 7

   • Why monolithic agents fail: managing context explosion
   • Multi-Agent Systems (MAS) and context isolation strategies
   • Agent roles: router, planner, executor
   • Agent-to-agent context compression techniques
   • Governance, guardrails, and inter-agent safety
   • Ethics, bias reduction, and source traceability
   • Case Studies: IBM Watson Orchestrate and enterprise context orchestration systems
   • Career pathways: Context Architect and AI Governance roles

8. Capstone Project & Certification

   Lesson 8

   • Capstone overview: building a multi-agent context-aware system
   • Project build: query router with financial calculations and policy-based RAG using n8n
   • Presentation, peer review, and expert feedback
   • Final assessment and AI+ Context Engineering certification