AI
Context Engineering
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.
Duration
½ day
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.
- 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.
| 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 |
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.