What You'll Learn
- Understand and apply the 6 types of context: Instructions, Examples, Knowledge, Memory, Tools, and Tool Results
- Design role-based prompts with clear objectives and behavioral requirements
- Craft few-shot and zero-shot prompts using positive and negative examples
- Inject structured domain knowledge, process workflows, and documents into agent prompts
- Architect short-term and long-term memory systems for multi-turn reasoning
- Use tool descriptions, parameters, and return values to integrate APIs and functions
- Handle tool outputs and chain results across multiple agentic steps
- Balance context length vs. token limits using summarization and prompt compression
- Implement agent orchestration frameworks like LangChain, CrewAI, and LangGraph
- Build modular, reusable, and scalable agent workflows for real-world use cases
- Debug and improve agents with self-reflection and context refresh strategies
- Complete a capstone project by building a full multi-context AI agent from scratch
Requirements
- No prior knowledge required
- A computer with internet access
- Eagerness to learn
Who This Course is For
- A Prompt Engineer who wants to move beyond templates into modular, agentic design
- A Software Developer or AI Engineer building multi-step LLM-based applications
- A Technical Product Manager designing features powered by agents or assistants
- A Data Scientist experimenting with autonomous decision-making systems
- An AI Enthusiast curious about how tools like LangChain, OpenAI Assistants, and CrewAI really work
- A Researcher or Educator looking for deeper insight into contextual design principles for agents
Your Instructor
Experienced Instructor
Professional Educator
An experienced instructor with expertise in this field and a passion for teaching.
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