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AI+ Context Engineering™

AI+ Context Engineering™
  • 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.
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Certificate Code

AP 3309

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
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.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  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

  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

  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

  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

  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

  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)

  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

  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

AI Tools Covered

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
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