AI+ Context Engineering

EDU Trainings s.r.o.

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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

Cieľová skupina

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

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