</> Hands-On Engineering
AI+ Context Engineering™
Master AI+ Context Engineering for Production-Grade AI Systems
Tech you'll master:
Python
LLMs
LangChain
Vector DBs
Cloud APIs
// What You'll Build
Engineering-First Curriculum
- 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.
// SPECS
Program Details
Duration
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
Prerequisites
A solid foundation in AI and machine learning concepts, proficiency in programming and data handling, familiarity with cloud platforms and IoT environments, and the ability to design, manage, and optimize contextual data, memory, and tool orchestration are essential for this course.
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
// CURRICULUM
Module Stack
Each module includes hands-on labs and real-world projects.
[01]
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
[02]
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
[03]
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
[04]
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
[05]
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
[06]
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)
[07]
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
[08]
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
Why Engineers Choose This
Career-Defining Skills
Go beyond prompts
Learn to engineer instructions, tools, memory, and state so AI behaves reliably.
Production-ready systems
Build RAG + context pipelines that reduce hallucinations and improve grounding.
Scale with efficiency
Master selection + compression to control token cost, latency, and performance.
Enterprise-safe AI
Apply PII controls, role-based filtering, and conflict resolution for compliant deployments.
Real deliverable
Complete a multi-agent capstone (n8n) with routing + calculations + policy RAG.
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