AI+ Context Engineering™ - eLearning (exam included)

AI+ Context Engineering™ - eLearning (exam included)

Master Context-Aware AI Systems with AI+ Context Engineering™

Advance your AI expertise beyond basic prompting to design, build, and deploy production-ready context-aware AI solutions. This certification teaches you how to craft robust context pipelines, manage memory and tools, and build scalable AI systems that deliver accurate, reliable, and efficient outcomes across real-world workflows. You’ll gain practical skills in Retrieval-Augmented Generation (RAG), vector databases, secure enterprise integration, multi-agent orchestration, and no-code context workflows—preparing you to lead the next wave of AI innovation in enterprise environments.

Master AI+ Context Engineering for Production-Ready AI Systems

• Context Strategy & System Design – Learn to architect advanced context frameworks that extend beyond simple prompting, effectively managing instructions, memory, tools, and knowledge to ensure consistent AI performance across sessions and workflows.
• Developing Context-Aware AI Solutions – Build practical expertise in creating context pipelines, RAG-based architectures, and intelligent memory systems that deliver accurate, grounded, and cost-effective AI outputs.
• Context Optimization & Control – Apply the Write-Select-Compress-Isolate (W-S-C-I) framework to improve relevance, minimize hallucinations, manage token efficiency, and scale AI systems with confidence.
• Secure Enterprise Integration – Discover how to deploy AI responsibly within enterprise environments using role-based access controls, compliance safeguards, secure memory handling, and seamless context orchestration.
• Designing Future-Ready Agents & Workflows – Prepare for advanced AI adoption by building multi-agent ecosystems, automated workflows, and scalable context-driven architectures that remain dependable as technologies evolve.

Why This Certification Is Important

• Move Beyond Basic Prompting – Learn how to structure instructions, tools, memory, and system state to ensure dependable and consistent AI behavior.
• Build Production-Ready Solutions – Design and implement RAG-driven context pipelines that minimize hallucinations and enhance output accuracy.
• Scale with Precision and Efficiency – Apply advanced selection and compression techniques to manage token usage, reduce latency, and optimize system performance.
• Enable Enterprise-Grade Compliance – Implement PII safeguards, role-based access controls, and conflict-resolution strategies for secure and compliant AI deployment.
• Deliver a Real-World Capstone – Complete a multi-agent project using n8n featuring intelligent routing, calculations, and policy-driven RAG integration.

Key Features

• Course and material in English
• Intermediate level (Category: AI+ Professional)
• 1 year access to the platform 24/7

• 8 hours of video lessons & multimedia resources
• 16 hours of study time recommendation
• eBooks, Audiobooks, Podcasts
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake included
• Certification of completion included

Tools explored:

• LangChain and LangGraph
• LlamaIndex
• Vector Databases (Pinecone, Chroma)
• n8n, Zapier, Make.com
• Embedding Models and RAG Pipelines
• No-Code Automation Platforms
• Enterprise Data and API Integrations

Learning Outcomes

• Foundations of Context Engineering (Beyond Prompting) – Discover how to architect, control, and refine AI context dynamically at runtime, moving beyond simple prompts to structured management of instructions, memory, tools, and system state for dependable AI performance.
• Context Optimization with the W-S-C-I Framework – Apply the core principles of Write, Select, Compress, and Isolate to enhance relevance, accuracy, efficiency, and safety in production-grade AI environments.
• Designing Memory Architectures for AI – Build effective short-term and long-term memory systems using vector databases, summarization techniques, and feedback mechanisms to support personalization, continuity, and complex reasoning.
• Retrieval-Augmented Generation (RAG) & Grounded AI – Develop reliable AI applications through RAG pipelines, embedding models, and vector databases to reduce hallucinations and deliver verifiable, domain-specific responses.
• End-to-End Context Pipelines & Orchestration – Construct comprehensive context workflows—from user query to retrieval, compression, response generation, and memory updates—leveraging tools such as LangChain, LangGraph, and LlamaIndex.

Who Should Enroll?

• AI Engineers & LLM Developers – Designed for practitioners ready to move beyond basic prompt engineering and build production-grade, context-aware AI systems using RAG, memory frameworks, tools, and orchestration strategies.
• Product Managers & AI Architects – Ideal for professionals delivering reliable AI-powered features who need a deep understanding of context pipelines, grounding techniques, cost management, and system-level design decisions.
• Data & Platform Engineers – Suited for engineers working with vector databases, embeddings, retrieval systems, and AI infrastructure who want to design scalable, efficient, and trustworthy context architectures.
• Enterprise & Solution Architects – Built for architects deploying AI in regulated or large-scale environments where security, compliance, cost optimization, and multi-agent coordination are critical.
• AI Consultants & Technical Leaders – Perfect for advisors guiding AI adoption who require practical insight into why context engineering—not just model selection—is the key differentiator in modern AI systems.
• Advanced No-Code & Automation Builders – A strong fit for professionals using platforms like n8n, Make, or Zapier who want to create dependable AI workflows and agent-based systems without heavy backend development.

Prerequisites

• Foundational Programming Skills – Experience with Python, Java, or comparable programming languages.
• Basic AI Understanding – Familiarity with core artificial intelligence and machine learning concepts.
• Data Processing Experience – Ability to manage datasets and apply basic data preprocessing methods.
• IoT Awareness – Understanding of Internet of Things (IoT) systems and applications.
• Cloud Platform Familiarity – Basic exposure to cloud-based AI tools and services.

Exam Details

• Duration: 90 minutes
• Passing :70% (35/50)
• Format: 50 multiple-choice/multiple-response questions
• Delivery Method: Online via proctored exam platform (flexible scheduling)
• Language: English

Course Content

Module 1: Foundations of Context Engineering

• Introduction to Context Engineering beyond traditional prompt engineering
• The shift from simple prompting to full context pipelines
• Core elements of context: instructions, knowledge, tools, and system state
• Short-term versus long-term memory in LLM-based systems
• Key advantages: grounding, relevance, continuity, and cost efficiency
• Use Case: Designing a context-aware AI travel assistant
• Hands-on: Creating system instructions and memory states for a role-based AI agent

Module 2: Context Management Frameworks & Methods

• The W-S-C-I framework: Write, Select, Compress, Isolate
• WRITE: Defining agent identity, persona, guardrails, and state control
• SELECT: High-precision retrieval and metadata filtering
• COMPRESS: Summarization, token optimization, and auto-compaction
• ISOLATE: Setting boundaries for safety, focus, and context protection
• Advanced retrieval strategies: hybrid search and semantic chunking
• Case Study: Memory systems in ChatGPT and Claude
• Hands-on: Applying context selection and compression with LangChain or LlamaIndex
  
  

Module 3: Context Pipelines, RAG & Grounded AI Architecture

• Designing the complete context pipeline (input → retrieval → compression → assembly → response → update)
• Deep dive into Retrieval-Augmented Generation (RAG) systems
• Working with vector databases such as Pinecone and Chroma, and embedding models
• Identifying grounding failures: hallucinations, context poisoning, distraction
• Mitigation techniques: reranking, provenance tracking, and context diagnostics
• Case Study: Anthropic’s Multi-Agent Researcher (MAR)
• Hands-on: Building a RAG pipeline with vector search and grounded outputs

Module 4: Optimization, Scaling & Enterprise Deployment

• Managing token usage and cost optimization strategies
• Context scaling and the Model Context Protocol (MCP)
• Security and compliance: PII filtering, redaction, and role-based access
• Conflict resolution and maintaining context consistency
• Handling multi-modal context (text, tables, PDFs, video transcripts)
• Case Studies: Walmart “Ask Sam” and Morgan Stanley Knowledge Assistant
• Hands-on: Implementing secure, role-based context filtering and retrieval
  
  

Module 5: Context Flow Design for Business & No-Code Users

• Converting business processes into AI-ready context workflows
• Context Flow Diagrams (CFDs) and Automated Workflow Architecture (AWA)
• Visual implementation of W-S-C-I using no-code tools (n8n, Make, Zapier)
• Using context templates for structured and consistent outputs
• Use Case: Building a dynamic customer onboarding assistant
• Case Studies: Airbnb support automation and HSBC SME lending
• Hands-on: Creating a context flow using no-code orchestration tools
  
  

Module 6: Industry Applications of Context Engineering

• Applying context engineering in regulated environments
• Healthcare: clinical decision support and PHI isolation
• Finance: compliance summarization, market analysis, and tool-based context
• Legal & education: precision retrieval and personalized learning systems
• Risk mitigation: handling context poisoning and context conflicts
• Designing advanced agent memory for long-horizon tasks
• Case Studies: Activeloop (Legal/IP) and Five Sigma (Insurance)
  
  

Module 7: Multi-Agent Systems & Future Architectures

• Why monolithic agents fail: managing context explosion
• Multi-Agent Systems (MAS) and context isolation strategies
• Agent roles: router, planner, executor
• Agent-to-agent context compression techniques
• Governance, guardrails, and inter-agent safety
• Ethics, bias reduction, and source traceability
• Case Studies: IBM Watson Orchestrate and enterprise context orchestration systems
• Career pathways: Context Architect and AI Governance roles

Module 8: Capstone Project & Certification

• Capstone overview: building a multi-agent context-aware system
• Project build: query router with financial calculations and policy-based RAG using n8n
• Presentation, peer review, and expert feedback
• Final assessment and AI+ Context Engineering certification

Licensing and accreditation

This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements.

Equity Policy

AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.

FAQ

Can I apply what I learn immediately in real-world environments?

Yes. The course teaches production-level frameworks for context design, memory systems, RAG architectures, and multi-agent workflows—practical skills you can implement right away.

What makes AI+ Context Engineering different from other AI programs?

This certification emphasizes building dependable AI systems—not just experimenting with models or prompts. It covers structured context management (W-S-C-I), grounding techniques, tooling integration, governance, security, and cost optimization.

What kinds of projects will I complete?

You’ll design and build RAG-powered context pipelines, develop no-code context workflows, implement enterprise-grade guardrails, and complete a multi-agent capstone featuring policy-driven RAG and intelligent routing.

How is the course structured to ensure skill mastery?

The curriculum progresses logically from foundational concepts to advanced patterns, architecture design, optimization strategies, and real-world deployment—supported by case studies and hands-on implementation.

How does this course enhance my career prospects?

It equips you for emerging roles such as Context Architect, RAG/AI Systems Architect, and AI Governance or Reliability Lead by teaching scalable, secure, and production-ready AI system design.

How Can AVC Help Foster an AI-Ready Culture?

While AI offers significant advantages, many organizations struggle with challenges like talent gaps, complex data environments, and system integration barriers. At AVC, we understand these obstacles and have tailored our certification programs to help businesses overcome them effectively.

Our strategic approach focuses on building a culture that embraces AI adoption and innovation. Through our industry-recognized certifications and in-depth training, we equip your workforce with the skills and knowledge needed to lead your organization confidently into an AI-powered future.

Customized for Impact: Our programs aren't one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles.

Practical, Real-World Learning: We prioritize hands-on experience over theory, using real-world projects and case studies. This approach ensures your team gains the confidence and capability to implement AI solutions effectively, driving innovation and measurable business outcomes.