Course Overview
TOPAI+ Context Engineering™ focuses on designing and managing high-quality context that drives accurate, reliable, and scalable AI system behavior. The course explores how prompts, memory, retrieval systems, and contextual signals influence model reasoning and outputs across real-world applications. It covers practical techniques to structure, inject, and optimize context for large language models and AI agents. The program emphasizes reducing hallucinations, improving relevance, and aligning AI responses with business and technical intent.
Scheduled Classes
TOP| 07/21/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 08/25/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 09/22/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 10/20/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 11/24/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 12/22/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand the role of context in shaping AI model behavior and decision-making
- Design effective prompt structures and contextual frameworks for LLMs
- Implement memory, retrieval, and grounding techniques for reliable AI responses
- Optimize context pipelines for AI agents and production-grade applications
- Address challenges such as hallucinations, bias, and context drift
Outline
TOPFoundations of Context Engineering – Introduction
- What is Context Engineering (Beyond Prompt Engineering)
- From Prompting to Context Pipelines: The 2025 Paradigm Shift
- The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
- Short-Term vs Long-Term Memory in LLM Systems
- Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
- Use Case: Context-Aware AI Travel Assistant
- Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent
Context Management Patterns & Techniques
- The W-S-C-I Framework: Write, Select, Compress, Isolate
- WRITE Strategy: Agent Identity, Persona, Guardrails, and State
- SELECT Strategy: Precision Retrieval & Metadata Filtering
- COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
- ISOLATE Strategy: Context Boundaries, Safety, and Focus
- Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
- Case Study: ChatGPT & Claude Memory Systems
- Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex
Context Pipelines, RAG & Grounding Architecture
- The End-to-End Context Pipeline (Input → Retrieval → Compression → Assembly → Response → Update)
- Retrieval-Augmented Generation (RAG) Architecture Deep Dive
- Vector Databases: Pinecone, Chroma & Embedding Models
- Grounding Failures: Hallucinations, Context Poisoning, Distraction
- Mitigation Techniques: Rerankers, Provenance, Context Forensics
- Case Study: Anthropic’s Multi-Agent Researcher (MAR)
- Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses
Optimization, Scaling & Enterprise Readiness
- Token Economy & Cost Optimization in Context Pipelines
- Context Scaling & the Model Context Protocol (MCP)
- Security & Compliance: PII Filtering, Redaction, Role-Based Access
- Conflict Resolution & Context Consistency
- Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
- Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
- Hands-on: Implement Role-Based Context Filtering and Secure Retrieval
Context Flow Design for Business Users (No-Code AI)
- Translating Business Processes into AI-Ready Context Flows
- Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
- Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
- Context Templates for Consistency & Structured Outputs
- Use Case: Dynamic Customer Onboarding Assistant
- Case Studies: Airbnb Support Automation & HSBC SME Lending
- Hands-on: Build a Context Flow Using No-Code Orchestration
Prerequisites
TOPRequired
- Familiarity with Python, Java, or similar languages
- Basic knowledge of machine learning and AI
- Ability to work with datasets and preprocessing techniques
- Familiarity with Internet of Things applications
- Basic knowledge of cloud-based AI services
Who Should Attend
TOP- AI Engineer
- Prompt Engineer
- ML Engineer
