Generative AI & Prompt Engineering
Duration: Part of the 2-month program
Outcomes
- Prompting (zero-shot, few-shot, CoT)
- RAG + vector databases
- LangChain + agentic systems
Quick facts
- Level
- Introductory → Intermediate
- Duration
- Part of the 2-month program
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Syllabus
Target audience
Students with no prior experience in AI/ML.
Delivery
Hands-on sessions facilitated by the instructor.
Modules
Module 1: Introduction to Generative AI
- Understanding Generative AI and its applications
- Overview of Large Language Models (LLMs)
- Ethical considerations in Generative AI
- Introduction to popular Generative AI tools
Module 2: Prompt Engineering Basics
- Crafting effective prompts for LLMs
- Techniques: Zero-shot, Few-shot, and Chain-of-Thought prompting
- Common pitfalls and how to avoid them
- Hands-on exercises with prompt design
Module 3: Retrieval-Augmented Generation (RAG)
- Understanding the RAG architecture
- Integrating external knowledge sources
- Implementing RAG with vector databases
- Use cases and best practices
Module 4: Introduction to LangChain
- Overview of LangChain framework
- Building applications with LangChain
- Integrating LLMs with external tools and APIs
- Hands-on project using LangChain
Module 5: Agentic AI and Autonomous Agents
- Concept of autonomous AI agents
- Designing and deploying AI agents
- Tools and frameworks for Agentic AI
- Ethical considerations and safety measures
Module 6: Advanced Prompt Engineering Techniques
- Exploring advanced prompting strategies
- Evaluating and refining prompt outputs
- Case studies of successful prompt engineering
- Hands-on exercises with complex prompts
Module 7: Capstone Project and Review
- Developing a Generative AI application from scratch
- Incorporating RAG, LangChain, and Agentic AI concepts
- Peer review and feedback sessions
- Final Q&A and course wrap-up
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