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100% fundable

AI+ Context Engineering Practitioner™

What an AI model knows shapes what it can do. Achieving consistent, reliable results means going beyond well-crafted prompts – it requires deliberately architected context that guides every model interaction.

Key topics

  • Designing and managing context architectures for AI systems
  • Building systematic prompt structures for repeatable outcomes
  • Working with context windows, role logic, and data prioritization
  • Minimizing errors and improving output stability in generative AI
  • Real-world use cases across development, automation, and analytics

Prerequisite
You can join without a technical background. Familiarity with AI concepts or digital workflows is a plus, but not a must.

Target group
Designed for those who want to shape how AI systems behave – whether you work in engineering, data, product, or innovation and are looking to produce more consistent, trustworthy AI output.

Mastering context engineering means taking real control over AI behavior. This certification gives you the skills to build AI interactions that are structured, transparent, and built to perform.

course content
  • How context engineering extends beyond traditional prompt engineering
  • The shift from prompts to context pipelines: what changed in 2025 and why
  • Four essential layers of AI context: instructions, knowledge, tools, and state
  • Understanding short-term and long-term memory in large language models
  • Key advantages: improved relevance, output consistency, and token cost control
  • Applied example: building a context-aware AI travel assistant
  • Practical exercise: draft system instructions and memory states for a role-based AI agent
  • Applying the W-S-C-I Framework: Write, Select, Compress, Isolate
  • WRITE in practice: shaping agent identity, persona, guardrails, and state
  • SELECT techniques: targeted data retrieval and metadata filtering
  • COMPRESS approaches: token efficiency through summarization and auto-compression
  • ISOLATE methods: defining context boundaries for security and precision
  • Advanced retrieval strategies: hybrid search and semantic chunking
  • Case study: how ChatGPT and Claude manage conversational memory
  • Hands-on exercise: apply context selection and compression using LangChain or LlamaIndex
  • Tracing the full context pipeline: from input through retrieval and compilation to response
  • How Retrieval-Augmented Generation (RAG) works – architecture explained in depth
  • Choosing and using vector databases: Pinecone, Chroma, and embedding models
  • Recognizing grounding failures: hallucinations, context poisoning, and distraction
  • Corrective measures: rerankers, provenance tracking, and context forensics
  • Case study: Anthropic’s Multi-Agent Researcher (MAR)
  • Hands-on exercise: assemble a RAG pipeline with vector search and grounded answer generation
  • Managing token costs for efficiency across context pipelines
  • Scaling strategies and the Model Context Protocol (MCP)
  • Privacy and compliance: PII detection, redaction, and role-based access control
  • Handling context conflicts and maintaining consistent model output
  • Processing multimodal inputs: documents, tables, and video transcripts
  • Enterprise examples: Walmart “Ask Sam” and Morgan Stanley Knowledge Assistant
  • Hands-on exercise: build role-based context filtering and secure data retrieval
  • How to convert business workflows into structured, AI-ready context flows
  • Building context flow diagrams (CFDs) and automated workflow architectures (AWA)
  • Visualizing the W-S-C-I model with no-code platforms (n8n, Make, Zapier)
  • Creating reusable context templates for consistent, well-structured AI output
  • Applied example: a dynamic AI assistant in the customer onboarding journey
  • Business case studies: Airbnb support automation and HSBC SME lending
  • Hands-on exercise: design your own context flow with no-code orchestration
  • What makes context engineering different in highly regulated sectors
  • Healthcare: AI-assisted diagnostics and strict isolation of sensitive patient data
  • Finance: market insights, regulatory summaries, and tool-augmented context
  • Legal and education: targeted document retrieval and individually adapted learning context
  • Managing risk: identifying context poisoning and resolving context conflicts
  • Persistent agent memory for complex, multi-step long-running tasks
  • Case studies: Activeloop in IP law and Five Sigma in the insurance sector
  • Why single-agent architectures hit a wall: the context explosion problem
  • How multi-agent systems (MAS) work – and how context isolation keeps them effective
  • Distributing work across agents: router, planner, and executor roles
  • Compressing and passing context efficiently between agents
  • Governance, guardrails, and cross-agent security mechanisms
  • Responsible AI in MAS: fairness, bias control, and source traceability
  • Enterprise examples: IBM Watson Orchestrate and Enterprise Context Orchestrators
  • Career outlook: context architect, AI orchestration lead, and governance specialist
  • Project overview: designing and implementing a multi-agent, context-sensitive system
  • Build phase: query router, financial calculations, and policy RAG with n8n
  • Present your solution, gather feedback, and refine your system
  • Complete the final assessment and earn the AI+ Context Engineering™ certification

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.