AI+ Context Engineering™

AI+ Context Engineering™

The AI+ Context Engineering™ course is designed to provide participants with a comprehensive understanding of how context shapes the performance, accuracy, and reliability of Artificial Intelligence (AI) systems. As AI applications become more advanced and widely adopted, the ability to design, structure, and manage contextual information is critical to achieving meaningful and relevant outputs.

This course introduces the concept of context engineering, focusing on how data, prompts, user intent, environment, and domain knowledge influence AI behavior. Participants will learn how to effectively design prompts, manage contextual inputs, and optimize interactions with AI models to produce more accurate, consistent, and valuable results across various use cases.

Through practical examples and hands-on exercises, learners will explore techniques for prompt engineering, context layering, memory utilization, and real-time adaptation in AI-driven systems. The course also covers the integration of contextual intelligence into workflows, enabling improved automation, personalization, and decision-making.

In addition, participants will examine challenges such as bias, data quality, and ethical considerations, ensuring responsible and effective use of AI technologies. By understanding how to control and refine context, professionals can significantly enhance the performance of AI tools in business, customer service, content generation, and operational environments.

Course Objectives

By the end of this course, participants will be able to:

• Understand the concept and importance of context in AI systems
• Design effective prompts and contextual frameworks
• Optimize AI outputs using structured context techniques
• Apply context engineering in real-world business scenarios
• Identify and mitigate bias, ambiguity, and context-related risks

Assessment & Certification

• Knowledge checks and quizzes
• Practical exercises
• Final assessment (optional)

Certification:
Participants will receive an AI+ Context Engineering™ Certificate of Completion

Target Audience

• AI/ML Practitioners (Beginner to Intermediate)
• Data Analysts & Business Analysts
• Product Managers & Project Managers
• Digital Transformation Professionals
• Content Creators & Automation Specialists
• Anyone working with AI tools (e.g., Chatbots, LLMs)

Course Outline:

Module 1: Foundations of Context Engineering – 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 → 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

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 “Ask Sam” & 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 Certification