Dive into Generative AI with prompt engineering, data visualization, MCP integration, and real-world automation apps.
What you will learn
Explain the core capabilities, strengths, and ideal use cases of Google Gemini, IBM Watson Analytics, and the Model Context Protocol (MCP).
Set up and confidently navigate Google Gemini and IBM Watson interfaces, including key settings, features, and workflow areas.
Apply clear prompt engineering frameworks to consistently generate high‑quality outputs in Gemini, including iterative refinement techniques.
Build practical no‑code automation workflows for tasks such as summarization, content drafting, data extraction, and productivity boosts using Gemini.
Import, explore, and prepare datasets in IBM Watson; create effective visualizations and communicate insights with clean, shareable dashboards.
Describe MCP fundamentals, architecture, and roles, and map common real‑world scenarios where MCP adds value—without writing code.
Plan and configure no‑code MCP‑enabled integrations to safely connect models with tools, data sources, and business workflows.
Compare Gemini vs. Watson for different tasks and select the right tool using clear decision criteria (data needs, output type, governance, and speed).
Execute iterative content generation workflows—from initial draft to structured review and finalization—using templates and checklists.
Identify and avoid common pitfalls across prompting, data hygiene, visualization clarity, and integration setup to ensure reliable outcomes.
Design intelligent, repeatable workflows that improve efficiency, reduce manual effort, and align with business or project goals.
Scope, plan, and present a mini capstone project that combines Gemini, Watson, and MCP, including goals, process, results, and next steps.
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- Course Overview
- Embark on a comprehensive journey to master the cutting edge of Generative AI, focusing on practical application development. This course demystifies the creation of sophisticated AI-powered solutions by diving deep into Large Language Models (LLMs), the powerful LangChain framework, and the innovative Retrieval-Augmented Generation (RAG) technique. You’ll go beyond theoretical understanding to architect and deploy real-world applications, including dynamic chatbots and intelligent automation tools.
- Uncover the art and science of prompt engineering, transforming raw data into actionable insights and creative outputs. Explore the strategic integration of data visualization to interpret complex AI models and communicate findings effectively. Learn to leverage the power of MCP (Multi-Cloud Platform) integration for scalable and resilient AI deployments, ensuring your applications can thrive in diverse cloud environments. This curriculum is designed for hands-on learners who want to build, test, and deploy tangible Generative AI solutions.
- Requirements / Prerequisites
- A foundational understanding of programming concepts, preferably Python, is highly recommended to fully grasp the implementation details and code examples.
- Familiarity with basic command-line operations will be beneficial for setting up environments and running scripts.
- A curious and proactive mindset, eager to experiment with AI models and explore their capabilities.
- Skills Covered / Tools Used
- AI Model Architecture: Deep dive into the underlying principles of transformer models and their application in modern NLP.
- Framework Mastery: Proficiency in utilizing the LangChain framework for orchestrating LLM workflows and building complex AI chains.
- Augmented Intelligence: Practical implementation of Retrieval-Augmented Generation (RAG) to enhance LLM knowledge bases and improve response accuracy.
- Vector Databases: Expertise in deploying and querying vector databases for efficient semantic search and knowledge retrieval.
- Application Development: Hands-on experience in building, testing, and deploying functional AI applications, including chatbots and automation tools.
- Data Interpretation: Skill in using data visualization techniques to analyze model behavior and present AI-driven insights.
- Prompt Optimization: Advanced strategies for crafting precise and effective prompts to unlock the full potential of generative models.
- Cloud Integration: Understanding the principles of Multi-Cloud Platform (MCP) integration for scalable AI solutions.
- Python Libraries: Practical application of key Python libraries essential for AI development and data manipulation.
- Benefits / Outcomes
- Transform your career by acquiring in-demand skills in one of the fastest-growing fields in technology.
- Empower yourself to build intelligent applications that can automate tasks, generate creative content, and provide sophisticated conversational experiences.
- Gain the confidence to tackle complex AI challenges and contribute to innovative projects in various industries.
- Develop a portfolio of practical Generative AI projects showcasing your ability to implement advanced concepts.
- Become a proficient architect of AI-powered solutions, capable of designing and deploying robust and scalable systems.
- PROS
- Practical, project-driven learning focused on building real-world applications.
- Cutting-edge curriculum covering the latest advancements in Generative AI and LLMs.
- Comprehensive skill development from foundational concepts to advanced deployment strategies.
- Hands-on experience with essential tools and frameworks like LangChain and RAG.
- CONS
- May require significant time investment for in-depth practice and project completion.
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