| Category | AI Development, AI Technical, All Courses, Available Now, Context Engineering Specialist, English, Language |
|---|---|
| Program Name | AI+ Context Engineering™ |
| Duration | Instructor-Led:1 day (live or virtual) | Self-Paced:8 hours of content |
| Prerequisites | Basic Programming Knowledge: Familiarity with Python, Java, or similar languages. Understanding of AI Concepts: Basic knowledge of machine learning and AI. Data Handling Skills: Ability to work with datasets and preprocessing techniques. Experience with IoT: Familiarity with Internet of Things applications. Familiarity with Cloud Platforms: Basic knowledge of cloud-based AI services |
| Exam Format | 50 questions, 70% passing, 90 minutes, online proctored exam |
Understand how to design, manage, and optimize AI context at runtime—moving past naive prompt engineering to systematic control of instructions, memory, tools, and state for reliable AI behavior.
Master the four core strategies—Write, Select, Compress, and Isolate—to control relevance, accuracy, cost, and safety in production AI systems.
Learn how to design short-term and long-term memory using vector databases, summarization, and feedback loops to enable continuity, personalization, and long-horizon reasoning.
Build grounded AI systems using RAG pipelines, embedding models, and vector databases to eliminate hallucinations and ensure responses are verifiable and domain-accurate.
Design end-to-end context pipelines—from user input to retrieval, compression, assembly, response, and memory updates—using tools like LangChain, LangGraph, and LlamaIndex.
Design and govern end-to-end context pipelines (Write, Select, Compress, Isolate), ensuring AI systems are grounded, reliable, cost-efficient, and compliant across enterprise use cases.
Own the architecture and implementation of context-aware AI systems, including RAG pipelines, memory strategies, and multi-agent orchestration, translating business requirements into production-ready AI flows.
Lead the delivery of context-driven AI solutions by aligning retrieval, memory, tooling, and orchestration strategies with organizational goals, performance constraints, and regulatory requirements.
Build and manage multi-agent and tool-integrated AI systems, ensuring clean context handoffs, isolation boundaries, and scalable orchestration using frameworks like LangChain, LangGraph, MCP, and no-code workflows.
Establish guardrails for context quality, grounding, security, and compliance—preventing hallucinations, context poisoning, and data leakage while enabling auditable, trustworthy AI at scale.
Yes. You’ll learn production-ready patterns for context, memory, RAG pipelines, and multi-agent workflows—skills you can apply right away.
It focuses on reliable AI systems, not just models or prompts—covering context management (W-S-C-I), grounding, tooling, governance, security, and cost control.
You’ll build and design RAG + context pipelines, context flows (no-code), enterprise guardrails, and a multi-agent capstone with policy RAG and tool-based routing.
Modules progress from foundations → patterns → architecture → optimization → real-world deployment, reinforced with case studies and hands-on builds.
It prepares you for roles like Context Architect, RAG/AI Systems Architect, and AI Governance/Reliability Lead by teaching scalable, compliant, production AI design.
70%
50 multiple-choice/multiple-response questions
