AI+ Context Engineering™
AP 3309
Master AI+ Context Engineering 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.
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
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 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
Job Roles & Industry Outlook
Context Architect
Design and govern end-to-end context pipelines (Write, Select, Compress, Isolate), ensuring AI systems are grounded, reliable, cost-efficient, and compliant across enterprise use cases.
AI Context Engineering Lead
Own the architecture and implementation of context-aware AI systems, including RAG pipelines, memory strategies, and multi-agent orchestration, translating business requirements into production-ready AI flows.
Context-Aware AI Solutions Manager
Lead the delivery of context-driven AI solutions by aligning retrieval, memory, tooling, and orchestration strategies with organizational goals, performance constraints, and regulatory requirements.
Enterprise AI Orchestration Specialist
Build and manage multi-agent and tool-integrated AI systems, ensuring clean context handoffs, isolation boundaries, and scalable orchestration using frameworks like LangChain, LangGraph, MCP, and no-code workflows.
AI Governance & Context Reliability Leader
Establish guardrails for context quality, grounding, security, and compliance—preventing hallucinations, context poisoning, and data leakage while enabling auditable, trustworthy AI at scale.
Skills You’ll Gain
- Context Architecture & Orchestration
- RAG System Design & Optimization
- Memory Engineering (Short-Term & Long-Term)
- Vector Databases & Retrieval Pipelines
- Token Cost Optimization & Context Compression
- Multi-Agent Context Isolation & Coordination
- Enterprise AI Security, Compliance & Governance
- No-Code Context Flow Design for Business Teams
What You'll Learn
- 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
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
Tools You'll Explore
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
Prerequisites
- Basic Programming Knowledge: Familiarity with Python, Java, or similar languages.
- Understanding of AI Concepts: Basic knowledge of machine learning and AI.
- Data Handling Skills: Ability to work with datasets and preprocessing techniques.
- Experience with IoT: Familiarity with Internet of Things applications.
- Familiarity with Cloud Platforms: Basic knowledge of cloud-based AI services
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Exam Blueprint:
- Foundations of Context Engineering – 7%
- Context Management Patterns & Techniques – 15%
- The Context Pipeline, RAG, and Grounding Architecture – 15%
- Optimization, Scaling, and Enterprise Readiness – 15%
- Context Flow Design for Business Users (No-Code AI) – 12%
- Real-World Industry Context Applications – 12%
- Multi-Agent Orchestration & The Future – 12%
- Capstone Project – 12%
Choose the Format That Fits Your Schedule
What’s Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
Instructor-Led (Live Virtual/Classroom)
- 1 day of intensive training with live demos
- Real-time Q&A, peer collaboration, and hands-on labs
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- ~8 hours of on-demand video lessons, e-book, podcasts, and interactive labs
- Learn anywhere, anytime, with modular quizzes to track progress
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Get CertifiedFrequently Asked Questions
Yes. You’ll learn production-ready patterns for context, memory, RAG pipelines, and multi-agent workflows—skills you can apply right away.
It focuses on reliable AI systems, not just models or prompts—covering context management (W-S-C-I), grounding, tooling, governance, security, and cost control.
You’ll build and design RAG + context pipelines, context flows (no-code), enterprise guardrails, and a multi-agent capstone with policy RAG and tool-based routing.
Modules progress from foundations → patterns → architecture → optimization → real-world deployment, reinforced with case studies and hands-on builds.
It prepares you for roles like Context Architect, RAG/AI Systems Architect, and AI Governance/Reliability Lead by teaching scalable, compliant, production AI design.
