AI+ Context Engineering™

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.

Módulos

  • Module 1: Foundations of Context Engineering – Introduction:
    1. 1.1 What is Context Engineering (Beyond Prompt Engineering)
    2. 1.2 From Prompting to Context Pipelines: The 2025 Paradigm Shift
    3. 1.3 The Four Building Blocks of Context: Instructions, Knowledge, Tools, State
    4. 1.4 Short-Term vs Long-Term Memory in LLM Systems
    5. 1.5 Benefits of Context Engineering: Grounding, Relevance, Continuity, Cost Control
    6. 1.6 Use Case: Context-Aware AI Travel Assistant
    7. 1.7 Hands-on: Designing System Instructions and Memory State for a Role-Based AI Agent
  • Module 2: Context Management Patterns & Techniques:
    1. 2.1 The W-S-C-I Framework: Write, Select, Compress, Isolate
    2. 2.2 WRITE Strategy: Agent Identity, Persona, Guardrails, and State
    3. 2.3 SELECT Strategy: Precision Retrieval & Metadata Filtering
    4. 2.4 COMPRESS Strategy: Summarization, Token Optimization, Auto-Compaction
    5. 2.5 ISOLATE Strategy: Context Boundaries, Safety, and Focus
    6. 2.6 Advanced Retrieval Patterns: Hybrid Search, Semantic Chunking
    7. 2.7 Case Study: ChatGPT & Claude Memory Systems
    8. 2.8 Hands-on: Implement Context Selection & Compression Using LangChain / LlamaIndex
  • Module 3: Context Pipelines, RAG & Grounding Architecture:
    1. 3.1 The End-to-End Context Pipeline (Input → Retrieval → Compression → Assembly → Response → Update)
    2. 3.2 Retrieval-Augmented Generation (RAG) Architecture Deep Dive
    3. 3.3 Vector Databases: Pinecone, Chroma & Embedding Models
    4. 3.4 Grounding Failures: Hallucinations, Context Poisoning, Distraction
    5. 3.5 Mitigation Techniques: Rerankers, Provenance, Context Forensics
    6. 3.6 Case Study: Anthropic’s Multi-Agent Researcher (MAR)
    7. 3.7 Hands-on: Build a RAG Pipeline with Vector Search and Grounded Responses
  • Module 4: Optimization, Scaling & Enterprise Readiness:
    1. 4.1 Token Economy & Cost Optimization in Context Pipelines
    2. 4.2 Context Scaling & the Model Context Protocol (MCP)
    3. 4.3 Security & Compliance: PII Filtering, Redaction, Role-Based Access
    4. 4.4 Conflict Resolution & Context Consistency
    5. 4.5 Multi-Modal Context: Text, Tables, PDFs, Video Transcripts
    6. 4.6 Case Studies: Walmart “Ask Sam” & Morgan Stanley Knowledge Assistant
    7. 4.7 Hands-on: Implement Role-Based Context Filtering and Secure Retrieval
  • Module 5: Context Flow Design for Business Users (No-Code AI):
    1. 5.1 Translating Business Processes into AI-Ready Context Flows
    2. 5.2 Context Flow Diagrams (CFDs) & Automated Workflow Architecture (AWA)
    3. 5.3 Implementing W-S-C-I Visually Using No-Code Tools (n8n / Make / Zapier)
    4. 5.4 Context Templates for Consistency & Structured Outputs
    5. 5.5 Use Case: Dynamic Customer Onboarding Assistant
    6. 5.6 Case Studies: Airbnb Support Automation & HSBC SME Lending
    7. 5.7 Hands-on: Build a Context Flow Using No-Code Orchestration
  • Module 6: Real-World Industry Context Applications:
    1. 6.1 Context Engineering in Regulated Domains
    2. 6.2 Healthcare: Clinical Decision Support & PHI Isolation
    3. 6.3 Finance: Market Analysis, Compliance Summarization & Tool-Based Context
    4. 6.4 Legal & Education: Precision Retrieval & Personalized Learning Context
    5. 6.5 Risk Mitigation: Context Poisoning & Context Clash
    6. 6.6 Advanced Agent Memory for Long-Horizon Tasks
    7. 6.7 Case Studies: Activeloop (Legal/IP) & Five Sigma (Insurance)
  • Module 7: Multi-Agent Orchestration & the Future:
    1. 7.1 Why Monolithic Agents Fail: Context Explosion
    2. 7.2 Multi-Agent Systems (MAS) & Context Isolation
    3. 7.3 Agent Roles: Router, Planner, Executor
    4. 7.4 Agent-to-Agent Context Compression
    5. 7.5 Guardrails, Governance & Inter-Agent Safety
    6. 7.6 Ethics, Bias Mitigation & Source Traceability
    7. 7.7 Case Studies: IBM Watson Orchestrate & Enterprise Context Orchestrators
    8. 7.8 Career Pathways: Context Architect & AI Governance Roles
  • Module 8: Capstone Project & Certification:
    1. 8.1 Capstone Overview: Multi-Agent Context-Aware System
    2. 8.2 Build: Query Router with Financial Calculations & Policy RAG (n8n)
    3. 8.3 Presentation, Review & Feedback
    4. 8.4 Final Evaluation & AI+ Context Engineering Certification

Herramientas de IA

  • 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
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