Prompt Engineering & Generative AI Applications 

This course builds the strong programming foundation required for AI, Machine Learning, and Agentic AI development. Participants progress from Python fundamentals and core data structures through dThis course develops mastery of interacting with, optimizing, and deploying Large Language Models. Participants cover generative AI fundamentals, prompt engineering, Retrieval-Augmented Generation (RAG), multimodal AI, and business applications spanning marketing, customer service, HR, and document intelligence. ata analysis with NumPy and Pandas, API-driven automation, and the AI libraries (OpenAI APIs, Hugging Face, LangChain) used to build real AI applications. 

Course Content

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Week 1: Generative AI Fundamentals 

• Introduction to generative models 

• LLM architecture overview 

• Tokens and embeddings 

• Common use cases 

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Week 2: Prompt Engineering Techniques 

• Zero-shot and few-shot prompting 

• Chain-of-thought prompting 

• Role and persona prompting

Week 3: Advanced Prompt Design 

• Prompt chaining 

• Structured outputs 

• Guardrails and prompt safety 

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Week 4: RAG Fundamentals 

• Vector embeddings 

• Vector databases 

• Retrieval pipeline design 

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Week 5: Building a RAG Knowledge Bot 

• Chunking strategies 

• Indexing and retrieval tuning 

• Evaluating RAG quality 

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Week 6: Multimodal AI 

• Image generation models 

• Vision-language models 

• Audio and speech AI 

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Week 7: AI Content Generation 

• Marketing copy generation 

• Content calendars 

• Brand voice tuning 

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Week 8: Business Applications I 

• Customer service AI assistants 

• HR assistants