Master Generative AI & Build Intelligent Applications
Explore the technology behind ChatGPT and modern Generative AI systems. Learn Prompt Engineering, Large Language Models, Python, AI APIs, Retrieval-Augmented Generation and AI Agents through practical application-building projects.
Understand how modern AI models generate text, images and other content.
Learn to design, test and improve effective AI prompts.
Connect language models to Python applications using APIs.
Practice building chatbots, document assistants and AI workflows.
Learn the Technology Powering the Generative AI Revolution.
Generative AI is changing how
people create content, develop
software, search information
and automate business workflows.
This course introduces
Generative AI fundamentals,
transformer-based language models,
Prompt Engineering,
LLM APIs, embeddings,
vector databases and
Retrieval-Augmented Generation.
Learn to create introductory
AI-powered applications,
understand model limitations,
evaluate outputs and explore
responsible AI development.
Generative AI Skills You'll Develop
Develop a practical understanding of modern Generative AI tools, language models and application development.
Prompt Engineering
Design structured prompts, use few-shot examples, refine model instructions and evaluate AI responses.
Large Language Models
Understand transformers, tokens, context windows, embeddings and model capabilities.
Python & LLM APIs
Integrate language models with Python applications using APIs and structured outputs.
Retrieval-Augmented Generation
Explore document processing, embeddings, vector search and knowledge-grounded AI responses.
AI Agents & Automation
Learn agent workflows, tool calling, task orchestration and automation concepts.
Generative AI Applications
Build introductory AI chatbots, assistants, content tools and document-based applications.
Generative AI Tools You'll Explore
Become familiar with tools, frameworks and concepts used in modern AI applications.
Generative AI Course Syllabus
A structured learning roadmap from Generative AI fundamentals to LLM-powered applications, RAG systems and AI agents.
Module 01 — Introduction to Generative AI
- What is Generative AI?
- AI vs ML vs Generative AI
- Text, image, audio and code generation
- Introduction to ChatGPT and LLMs
- Common Generative AI use cases
- Benefits and limitations
- Responsible AI fundamentals
Module 02 — Prompt Engineering
- Understanding prompts and instructions
- Prompt structure and clarity
- Zero-shot prompting
- Few-shot prompting
- Role and context prompting
- Structured output prompting
- Prompt evaluation and iteration
- Reducing unreliable responses
Module 03 — Python for Generative AI
- Python programming fundamentals
- Functions and modules
- Working with JSON
- HTTP requests and APIs
- Environment variables
- API key management
- Handling errors and retries
- Building simple AI scripts
Module 04 — Large Language Models
- Introduction to LLMs
- Transformer architecture overview
- Tokens and tokenization
- Context windows
- Attention mechanism concepts
- Pretraining and fine-tuning overview
- Open-weight vs hosted models
- Model selection considerations
Module 05 — Building Applications with LLM APIs
- Understanding LLM API requests
- Connecting Python to an LLM API
- System and user instructions
- Managing conversation history
- Structured JSON outputs
- Streaming responses overview
- Cost and usage considerations
- Basic AI chatbot project
Module 06 — Embeddings & Vector Databases
- What are embeddings?
- Semantic similarity
- Vector representations
- Similarity search
- Introduction to vector databases
- FAISS fundamentals
- Document chunking strategies
- Storing and retrieving knowledge
Module 07 — Retrieval-Augmented Generation
- Introduction to RAG
- Why retrieval is useful
- Document ingestion
- Text splitting and chunking
- Generating embeddings
- Retrieving relevant context
- Grounding model answers in documents
- Evaluating retrieval quality
- Building a PDF question-answering assistant
Module 08 — LangChain & AI Agents
- Introduction to LangChain
- Prompt templates
- Chaining model operations
- Tool calling fundamentals
- Introduction to AI agents
- Agent planning and execution concepts
- Workflow orchestration
- Agent guardrails and permissions
- Testing agent behavior
Module 09 — AI Application Development & Deployment
- Designing a Generative AI application
- Building interfaces with Streamlit
- Connecting a frontend to an AI backend
- Conversation state management
- Input validation and error handling
- Protecting sensitive information
- Prompt injection awareness
- Deployment fundamentals
- Monitoring quality and costs
Module 10 — Generative AI Capstone Projects
- Choosing an AI application idea
- Defining project requirements
- Selecting an LLM
- Designing prompts and workflows
- Integrating external knowledge
- Building and testing the application
- Evaluating outputs and safety
- Documenting the project
- GitHub portfolio preparation
- Final project presentation
Build Practical Generative AI Projects
Apply LLMs, APIs, retrieval and prompt engineering through realistic AI application exercises.
AI-Powered Chatbot
Build a conversational chatbot using Python and an LLM API. Practice handling prompts, maintaining conversation context and displaying responses.
PDF Document AI Assistant
Create a document-based question-answering assistant using embeddings, vector search and Retrieval-Augmented Generation.
AI Content & Workflow Assistant
Build an AI application that generates structured drafts, summarizes information and assists with repetitive content workflows.
Build Skills for Generative AI Development Careers.
Generative AI is used in
software products,
customer support,
enterprise knowledge systems,
content workflows and
business automation.
Skills in Python,
LLM APIs, Prompt Engineering,
RAG and AI application
development can support
emerging technical roles.
Advanced roles typically
require software engineering,
system design, model evaluation
and deployment experience.
Questions About Generative AI
Can beginners learn Generative AI?
Does the course cover ChatGPT and Prompt Engineering?
Is Python required for Generative AI?
Will I learn RAG and Vector Databases?
Does the course include AI Agents?
Are practical projects included?
Ready to Build with Generative AI?
Explore Generative AI training with Placify India and develop practical skills in Prompt Engineering, Large Language Models, RAG, AI Agents and intelligent application development.