AI+ Context Engineering Practitioner™

$195.00

Formerly known as AI+ Context Engineering™ Master AI+ Context Engineering Practitioner™ for Production-Grade AI Systems

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.

Learning Modules

Module 1: Foundations of Context Engineering u2013 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 u2192 Retrieval u2192 Compression u2192 Assembly u2192 Response u2192 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: Anthropicu2019s 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 u201cAsk Samu201d & 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 Practitioneru2122 Certification

AI Enabled Coordinated Assurance Certification

Certificate Code:

AP 3309

Duration:

Instructor-Led:u00a01u00a0dayu00a0(live or virtual)
Self-Paced:u00a08u00a0hoursu00a0of 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.

Why This Certification Matters

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.

Who Should Enroll

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 contextu2014not just modelsu2014is 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