AI+ Context Engineering™

Price
Net: 397,00 €
VAT.: 75,43 €

Duration
1 day

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
WalkIn^®

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Context determines the quality, precision, and impact of AI results. Modern applications require structured, controllable, and traceable interactions between humans and models. This is precisely where a practical understanding of context engineering comes into play.

Key topics

Structuring and controlling AI contexts.
Prompt design with a systematic approach.
Context windows, role logic, and information weighting.
Error reduction and result stability in generative AI.
Application scenarios in development, automation, and analysis.

Prerequisite
Basic understanding of AI, generative models, and digital workflows. Technical depth is helpful but not essential.

Target audience
Specialists from development, data, product, innovation, digitalization, and AI-related roles with an interest in precise AI control.

Context engineering is becoming a decisive factor for reliable AI systems. A sound approach to context creates clarity, efficiency, and quality in demanding AI applications.

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course content

Fundamentals of Context Engineering – Introduction

What is context engineering (beyond prompt engineering)?
From prompting to context pipelines: The paradigm shift of 2025
The four building blocks of context: instructions, knowledge, tools, state
Short-term vs. long-term memory in LLM systems
Advantages of context engineering: down-to-earthness, relevance, continuity, cost control
Use case: Context-aware AI travel assistant
Practical exercise: Designing system instructions and memory states for a role-based AI agent.

Context management patterns and techniques

The W-S-C-I Framework: Write, Select, Compress, Isolate
WRITE strategy: Agent identity, persona, guardrails, and status
SELECT strategy: Precise retrieval and metadata filtering
COMPRESS Strategy: Summary, Token Optimization, Automatic Compression
ISOLATE strategy: Context boundaries, security, and focus
Advanced retrieval patterns: Hybrid search, semantic chunking
Case study: ChatGPT and Claude memory systems
Practical exercise: Implementing context selection and compression with LangChain/LlamaIndex.

Context pipelines, RAG, and grounding architecture

The end-to-end context pipeline (input → retrieval → compression → compilation → response → update)
Retrieval-Augmented Generation (RAG) architecture in detail
Vector databases: Pinecone, Chroma, and embedding models
Grounding errors: hallucinations, context poisoning, distraction
Remedial measures: Rerankers, provenance, context forensics
Case study: Anthropic's Multi-Agent Researcher (MAR)
Practical exercise: Building a RAG pipeline with vector search and grounded responses.

Optimization, scaling, and business maturity

Token economics and cost optimization in context pipelines.
Context scaling and the Model Context Protocol (MCP).
Security and compliance: PII filtering, redaction, role-based access
Conflict resolution and context consistency
Multimodal context: text, tables, PDFs, video transcripts
Case studies: Walmart "Ask Sam" and Morgan Stanley Knowledge Assistant
Practical exercises: Implementation of role-based context filtering and secure retrieval.

Context flow design for business users (no-code AI)

Translation of business processes into AI-enabled context flows.
Context flow diagrams (CFDs) and automated workflow architecture (AWA).
Visual implementation of W-S-C-I with no-code tools (n8n / Make / Zapier).
Context templates for consistency and structured output.
Use case: Dynamic assistant for customer onboarding.
Case studies: Support automation at Airbnb and SME lending at HSBC.
Hands-on exercise: Creating a context flow with no-code orchestration.

Applications in real industrial contexts

Context engineering in regulated areas.
Healthcare: Support for clinical decisions and isolation of personal health data.
Finance: Market analysis, summarization of compliance information, and tool-based context.
Legal and education: Accurate information retrieval and personalized learning context.
Risk mitigation: Context poisoning and context conflicts
Extended agent memory for long-term tasks.
Case studies: Activeloop (law/IP) and Five Sigma (insurance)

Multi-agent orchestration and the future

Why monolithic agents fail: Context explosion
Multi-agent systems (MAS) and context isolation
Agent roles: router, planner, executor
Agent-to-agent context compression
Guidelines, governance, and security between agents
Ethics, bias mitigation, and source traceability
Case studies: IBM Watson Orchestrate and Enterprise Context Orchestrators
Career paths: Context architect and AI governance roles

Capstone Project & Certification

Capstone Overview: Multi-Agent Context-Sensitive System
Creation: Query Router with Financial Calculations & Policy RAG (n8n)
Presentation, review & feedback
Final Assessment & Certification in AI+ Context Engineering

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently asked questions

What does "AI+ Context Engineering™" mean?

AI+ Context Engineering™ is a specialized certification that demonstrates how to build AI systems that understand and utilize context. This includes context architecture, RAG pipelines, memory and tool management, and optimization for real-world applications.

Why is context engineering more important than prompt engineering?

Prompt engineering focuses on good instructions. Context engineering organizes the entire environment of the AI response—including memory, tools, and data. This allows the AI to work more consistently and reliably.

What skills do you learn in the AI+ Context Engineering™ certification?

The certification teaches skills such as context architecture, RAG systems, memory engineering, token optimization, multi-agent design, and enterprise integration.

Who should pursue Context Engineering certification?

Primarily developers, AI architects, product managers, data engineers, and technical executives who want to work with complex AI systems.

What roles and jobs are created by context engineering?

New roles such as "context architect," "AI context engineering lead," and "enterprise AI orchestration specialist" demonstrate the demand for experts in this field.

How does context engineering fit into companies?

Companies use context engineering to make AI systems secure, compliant, and effective—for example, with access controls, rules, and data governance.

Is context engineering only relevant for developers?

No. Product managers, strategists, and technical decision-makers also benefit because Context Engineering bridges the gap between AI technology and business requirements.

Do you have any further questions? Please contact us.

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