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AI+ Context Engineering Practitioner™ certification badge

AP 3309

AI+ Context Engineering Practitioner™

Formerly known as AI+ Context Engineering™

Master AI+ Context Engineering Practitioner™ for Production-Grade AI Systems
AI DevelopmentAI Professional
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At a glance

What is included

Instructor-led OR Self-paced course + Official exam + Digital badge

Duration

  • Instructor-Led: 1 day (live or virtual)
  • Self-Paced: 8 hours of content

Recommended background

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.

Examination

50 questions, 70% passing, 90 minutes, online proctored exam

About this certification

  • 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.

Curriculum

Module 1: Foundations of Context Engineering – Introduction+
  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
Module 2: Context Management Patterns & Techniques+
  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
Module 3: Context Pipelines, RAG & Grounding Architecture+
  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
Module 4: Optimization, Scaling & Enterprise Readiness+
  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
Module 5: Context Flow Design for Business Users (No-Code AI)+
  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
Module 6: Real-World Industry Context Applications+
  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)
Module 7: Multi-Agent Orchestration & the Future+
  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
Module 8: Capstone Project & Certification+
  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 Practitioner™ Certification

Who should enrol

AI Engineers & LLM Developers: Built for practitioners who want to move beyond basic prompt engineering and design production-grade, context-aware AI systems using RAG, memory, tools, and orchestration patterns

Product Managers & AI Architects: Ideal for professionals responsible for shipping reliable AI features who need to understand context pipelines, grounding, cost control, and system-level design tradeoffs rather than toy demos

Data & Platform Engineers: For engineers working with vector databases, embeddings, retrieval systems, and AI infrastructure who want to architect scalable, efficient, and trustworthy context flows

Enterprise & Solution Architects: Designed for architects building AI systems in regulated or large-scale environments who must manage security, compliance, cost optimization, and multi-agent orchestration

AI Consultants & Technical Leaders: For professionals advising organizations on AI adoption who need a deep, practical understanding of why context—not just models—is the real differentiator in modern AI systems

Advanced No-Code / Automation Builders: A strong fit for builders using tools like n8n, Make, or Zapier who want to design reliable AI workflows and agentic systems without writing heavy infrastructure code

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