AI+ Context Engineering™ eLearning

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.

Duur: 1 dagen

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

Go beyond promptsLearn to engineer instructions, tools, memory, and state so AI behaves reliably.
Production-ready systemsBuild RAG + context pipelines that reduce hallucinations and improve grounding.
Scale with efficiencyMaster selection + compression to control token cost, latency, and performance.
Enterprise-safe AIApply PII controls, role-based filtering, and conflict resolution for compliant deployments.
Real deliverableComplete a multi-agent capstone (n8n) with routing + calculations + policy RAG.

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

Module 5: Context Flow Design for Business Users (No-Code AI)

Module 6: Real-World Industry Context Applications

Module 7: Multi-Agent Orchestration & the Future

Module 8: Capstone Project & Certification

Tools you will explore

Instructor-led OR Self-paced course + Official exam + Digital badge

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

A solid foundation in AI and machine learning concepts, proficiency in programming and data handling, familiarity with cloud platforms and IoT environments, and the ability to design, manage, and optimize contextual data, memory, and tool orchestration are essential for this course.

€ 200,00 excl. BTW

Artikelnummer: AP-3309-E Categorie: eLearning