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Advanced Prompt and Context Engineering: Practical Enterprise AI Capability Building
An advanced program for professionals moving beyond writing prompts to building reliable, scalable, production-ready AI systems. It combines advanced prompting, context engineering, RAG, memory, Model Context Protocol (MCP), tool calling, AI agents, evaluation and governance.
Six capabilities this program is built to give you as you design production-ready, context-aware AI systems.
Design system prompts, user context, memory and retrieved knowledge as one coherent architecture.
Ground AI assistants in your enterprise knowledge using Retrieval Augmented Generation.
Implement session and long-term memory so AI applications stay coherent across interactions.
Use Model Context Protocol to connect AI models to enterprise systems and APIs.
Design autonomous and multi-agent workflows with clear boundaries and human checkpoints.
Benchmark prompt quality and defend AI systems against injection and unsafe outputs.
How the program moves you from comparing prompts and context to shipping a complete enterprise AI solution.
Compare prompt engineering and context engineering as foundations of modern AI systems.
Watch a complete AI application built live using prompts, RAG, MCP and agents.
Design context pipelines, memory systems and retrieval workflows of your own.
Build a knowledge-aware AI assistant connected to real enterprise tools.
Create autonomous and multi-agent workflows with approval checkpoints.
Build and present a production-ready AI application using the full architecture stack.
Compare prompt engineering and context engineering as foundations of modern AI systems.
Watch a complete AI application built live using prompts, RAG, MCP and agents.
Design context pipelines, memory systems and retrieval workflows of your own.
Build a knowledge-aware AI assistant connected to real enterprise tools.
Create autonomous and multi-agent workflows with approval checkpoints.
Build and present a production-ready AI application using the full architecture stack.
The real architecture challenges you'll work through, pulled directly from the program's applied curriculum.
Design, build and present a production-ready AI application that combines advanced prompting, context engineering, RAG, memory, MCP, tool integration, AI agents, evaluation and governance.
The program centers on RAG, MCP and agent frameworks, not on any single AI vendor.
ChatGPT · Claude · Gemini · Copilot · Grok · DeepSeek
Perplexity · NotebookLM · Vector Databases · RAG
OpenAI API · Anthropic API · Google AI Studio · Azure AI Foundry · MCP
LangChain · LangGraph · LlamaIndex · Semantic Kernel
OpenAI Agents SDK · Copilot Studio · n8n · Zapier · Make
Vector databases · Memory frameworks · Prompt & Context libraries
A preview of the ten modules inside the program. See the full breakdown in the detailed curriculum.
How prompts, context, memory and reasoning work together in modern AI systems; prompt engineering vs. context engineering.
Zero-shot, few-shot, Chain of Thought, Tree of Thought, ReAct, Reflection, Step-Back and Least-to-Most.
Structure, manage, retrieve and optimize context; design system prompts, user context, memory and output constraints.
Build RAG workflows using enterprise knowledge sources.
Short-term, long-term, session memory and context persistence.
Generate JSON, XML, Markdown, tables and API-ready responses; connect AI with APIs and enterprise systems.
Connect AI to enterprise tools using MCP for interoperability.
Design autonomous AI agents and multi-agent workflows using context-aware reasoning.
Test, benchmark and continuously improve prompt and context performance; defend against prompt injection and unsafe outputs.
Build governance, standards and a production-ready AI application combining prompts, RAG, MCP, agents and evaluation.
Turn AI access into daily time savings across writing, summarization, research, and structured preparation.
Practical mastery of enterprise AI assistants tailored to workplace tasks and professional quality standards.
Learn systematic prompt design, role framing, constraints, examples, and iterative refinement.
Leverage AI for deep literature review, market intelligence, structured comparison, and rigorous source verification.
Use AI to clean data, uncover trends, formulate hypotheses, test assumptions, and prepare decision briefs.
Generic AI training is failing the enterprise. A finance director, an HR manager, and a software engineer require fundamentally different AI capabilities. Discover why role-based enablement—anchored in actual workflows, decisions, and domain expertise—is the only sustainable path to enterprise AI value.
Most enterprises conflate AI literacy (knowing what AI is) with AI capability (knowing how to use AI effectively in your role). This confusion leads to generic training that fails to change behaviour or drive ROI. Discover how to build true AI capability.
Enterprise AI initiatives are failing at scale not due to technological flaws, but because traditional IT training models are entirely unsuited for probabilistic AI tools. Discover why a human-centred capability building approach is essential.
Every module, topic, activity and practical application, laid out in the detailed curriculum.