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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.
Enroll Now
AI+ Context Engineering™
Self-Paced Online
USD $ 195.00
Instructor-Led Online
USD $ 1110.00

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Category
AI Development
AI Technical
All Courses
Context Engineering Specialist
English
Language
Program Name
AI+ Context Engineering™
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 Format
Exam details not available.

What You'll Learn

Context Engineering Foundations (Beyond Prompting)

Understand how to design, manage, and optimize AI context at runtime—moving past naive prompt engineering to systematic control of instructions, memory, tools, and state for reliable AI behavior.

Context Management Strategies (W-S-C-I Framework)

Master the four core strategies—Write, Select, Compress, and Isolate—to control relevance, accuracy, cost, and safety in production AI systems.

Memory Architecture for AI Systems

Learn how to design short-term and long-term memory using vector databases, summarization, and feedback loops to enable continuity, personalization, and long-horizon reasoning.

Retrieval-Augmented Generation (RAG) & Grounding

Build grounded AI systems using RAG pipelines, embedding models, and vector databases to eliminate hallucinations and ensure responses are verifiable and domain-accurate.

Context Pipelines & Orchestration

Design end-to-end context pipelines—from user input to retrieval, compression, assembly, response, and memory updates—using tools like LangChain, LangGraph, and LlamaIndex.

Certification Modules

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 Certification

Bitcoin+ Everyone™

certificate

Industry Opportunities

Opportunity Image

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.

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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.

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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.

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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.

Opportunity Image

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.

Frequently Asked Questions

Can I apply what I learn in this course to real-world scenarios immediately?
Yes. You’ll learn production-ready patterns for context, memory, RAG pipelines, and multi-agent workflows—skills you can apply right away.
What makes AI+ Context Engineering different from other AI courses?
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.
What type of projects will I work on?
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.
How is the course structured to ensure I learn the skills?
Modules progress from foundations → patterns → architecture → optimization → real-world deployment, reinforced with case studies and hands-on builds.
How does this course prepare me for the job market?
It prepares you for roles like Context Architect, RAG/AI Systems Architect, and AI Governance/Reliability Lead by teaching scalable, compliant, production AI design.

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

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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%
Self-Paced Online

Self-Paced: 8 hours of content

USD $ 195.00
Purchase Self-Paced Course
Instructor-Led Online

Instructor-Led: 1 day (live or virtual)

USD $ 1110.00
Purchase Instructor-Led Course

Core AI Tools Covered

LangChain and LangGraph

LangChain and LangGraph

LlamaIndex

LlamaIndex

Vector Databases (Pinecone, Chroma)

Vector Databases (Pinecone, Chroma)

n8n, Zapier, Make.com

n8n, Zapier, Make.com

Embedding Models and RAG Pipelines

Embedding Models and RAG Pipelines

No-Code Automation Platforms

No-Code Automation Platforms

Enterprise Data and API Integrations

Enterprise Data and API Integrations