Artificial Intelligence
Context Engineering
Overview
This course introduces participants to the emerging discipline of Context Engineering—the science and art of designing, managing, and optimizing context to improve human–AI collaboration, communication systems, and decision-making frameworks. Participants will learn how to define, shape, and utilize context in natural language processing (NLP), prompt design, and real-world problem-solving environments.
This course is included in the AI Annual Learning Pass.
Schedule
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Duration: 1/2 Day
Dates
Times
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Who Should Take This Course
Audience
This course is ideal for:
- AI practitioners and data scientists interested in advanced prompt engineering and contextual reasoning.
- Educators, researchers, and writers who leverage AI tools for teaching, content creation, and analysis.
- Business analysts, product managers, and consultants seeking to improve decision workflows through AI-assisted context design.
- Developers and technologists integrate large language models (LLMs) into applications that require nuanced contextual understanding.
Why You Should Take This Course
Learning Objectives
- Gain a cutting-edge skillset: Context Engineering is the next evolution beyond prompt engineering—helping you design systems that adapt dynamically to changing inputs and goals.
- Enhance communication with AI systems: Learn how to craft precise, layered, and responsive contexts to get consistent, high-quality outputs from LLMs.
- Improve human–machine collaboration: Discover how context can be engineered to reflect intent, tone, and domain knowledge.
- Hands-on learning: Through interactive activities and live demos, participants will engineer contexts for different use cases—from education to automation.
Course Outline
Context Engineering
| Module | Key Topics | Activities |
| Module 1: Introduction to Context Engineering | Definition of context; Layers of context (linguistic, situational, technical); Context in AI systems | Instructor demo: Analyzing prompt performance with and without context |
| Module 2: Context Design Frameworks | Context mapping; Intent modeling; Context hierarchies; Dynamic context switching | Breakout: Build a context map for an AI writing assistant |
| Module 3: Context in AI Communication | Context compression; Maintaining continuity in dialogue systems; Prompt chaining | Group activity: Design a multi-step prompt with evolving context |
| Module 4: Engineering for Domain Contexts | Applying context in education, healthcare, business, and creative domains | Case study: Context-aware AI tutoring system |
| Module 5: Tools and Techniques | AI tools for context management; Embedding frameworks; Retrieval-Augmented Generation (RAG); Metadata-driven context | Demo: Using a vector database to engineer persistent context |
| Wrap-up & Q/A | Review key principles; Certification quiz; Next steps and resources | Open Q/A and reflection exercise |